diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst
index 3412e5ac5..5c1305cc1 100644
--- a/docs/api/datasets.rst
+++ b/docs/api/datasets.rst
@@ -46,6 +46,7 @@ Available Datasets
     datasets/pyhealth.datasets.BMDHSDataset
     datasets/pyhealth.datasets.COVID19CXRDataset
     datasets/pyhealth.datasets.ChestXray14Dataset
+    datasets/pyhealth.datasets.HAM10000Dataset
     datasets/pyhealth.datasets.TUABDataset
     datasets/pyhealth.datasets.TUEVDataset
     datasets/pyhealth.datasets.ClinVarDataset
diff --git a/docs/api/datasets/pyhealth.datasets.HAM10000Dataset.rst b/docs/api/datasets/pyhealth.datasets.HAM10000Dataset.rst
new file mode 100644
index 000000000..a20d320c7
--- /dev/null
+++ b/docs/api/datasets/pyhealth.datasets.HAM10000Dataset.rst
@@ -0,0 +1,20 @@
+pyhealth.datasets.HAM10000Dataset
+===================================
+
+The **HAM10000** (Human Against Machine with 10000 training images) dataset is a large
+collection of multi-source dermatoscopic images of pigmented skin lesions. It is a
+widely used benchmark dataset for computer-aided melanoma detection and general
+dermatology image classification research.
+
+Each sample includes an image and associated metadata describing the lesion diagnosis,
+patient sex, age, and localization.
+
+Refer to the original dataset and documentation for more information:
+
+- Dataset page: https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000
+- Dataset paper: *Tschandl et al., “The HAM10000 dataset: A large collection of multi-source dermatoscopic images of pigmented lesions.”* Scientific Data, 2018.  
+
+.. autoclass:: pyhealth.datasets.HAM10000Dataset
+    :members:
+    :undoc-members:
+    :show-inheritance:
\ No newline at end of file
diff --git a/pyhealth/datasets/__init__.py b/pyhealth/datasets/__init__.py
index 7d6a65f16..e99eb6578 100644
--- a/pyhealth/datasets/__init__.py
+++ b/pyhealth/datasets/__init__.py
@@ -84,3 +84,4 @@ def __init__(self, *args, **kwargs):
     load_processors,
     save_processors,
 )
+from .ham10000 import HAM10000Dataset
\ No newline at end of file
diff --git a/pyhealth/datasets/configs/ham10000.yaml b/pyhealth/datasets/configs/ham10000.yaml
new file mode 100644
index 000000000..68aa9852e
--- /dev/null
+++ b/pyhealth/datasets/configs/ham10000.yaml
@@ -0,0 +1,13 @@
+version: "1.0"
+tables:
+  ham10000:
+    file_path: "ham10000-metadata-pyhealth.csv"
+    patient_id: "lesion_id"
+    timestamp: null
+    attributes:
+      - "image"
+      - "path"
+      - "label" 
+      - "age"
+      - "sex"
+      - "localization"
\ No newline at end of file
diff --git a/pyhealth/datasets/ham10000.py b/pyhealth/datasets/ham10000.py
new file mode 100644
index 000000000..3b6fc6d6c
--- /dev/null
+++ b/pyhealth/datasets/ham10000.py
@@ -0,0 +1,120 @@
+"""
+PyHealth dataset class for the HAM10000 dermoscopic lesion dataset.
+
+Dataset link:
+    https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000
+
+Dataset paper:
+    Philipp Tschandl et al. "HAM10000 Dataset: A large collection
+    of multi-source dermatoscopic images of pigmented lesions."
+    Scientific Data, 2018.
+
+This dataset contains 10,015 dermoscopic lesion images across 7 classes:
+    akiec: Actinic keratoses and intraepithelial carcinoma
+    bcc: Basal cell carcinoma
+    bkl: Benign keratosis-like lesions
+    df: Dermatofibroma
+    mel: Melanoma
+    nv: Melanocytic nevi
+    vasc: Vascular lesions
+
+Author:
+    Kacper Dural
+"""
+
+import os
+import logging
+from pathlib import Path
+from typing import Optional, List
+
+import pandas as pd
+
+from pyhealth.datasets import BaseDataset
+from pyhealth.processors import ImageProcessor
+from pyhealth.tasks import BaseTask
+
+logger = logging.getLogger(__name__)
+
+
+class HAM10000Dataset(BaseDataset):
+    """Dataset class for the HAM10000 dermoscopic image dataset."""
+
+    classes: List[str] = ["akiec", "bcc", "bkl", "df", "mel", "nv", "vasc"]
+
+    def __init__(
+        self,
+        root: str,
+        config_path: Optional[str] = None,
+    ):
+        """
+        Args:
+            root: dataset root directory containing:
+                - images/ folder with .jpg images
+                - metadata.csv describing samples
+
+            config_path: path to dataset config (optional)
+        """
+        self.root = root
+        self.image_dir = os.path.join(root, "images")
+        self.meta_path = os.path.join(root, "HAM10000_metadata.csv")
+
+        if not os.path.exists(self.meta_path):
+            raise FileNotFoundError("metadata.csv not found in dataset root.")
+
+        if not os.path.exists(self.image_dir):
+            raise FileNotFoundError("images/ folder not found in dataset root.")
+
+        # build internal metadata table
+        self._index_data()
+
+        super().__init__(
+            root=root,
+            tables=["ham10000"],
+            dataset_name="HAM10000",
+            config_path=config_path,
+        )
+
+    @property
+    def default_task(self) -> BaseTask:
+        """Default task: multiclass dermatology label classification."""
+        from pyhealth.tasks import image_multiclass_classification_fn
+        return image_multiclass_classification_fn
+
+    def set_task(self, *args, **kwargs):
+        """Attach an ImageProcessor if user does not supply one."""
+        input_processors = kwargs.get("input_processors", None) or {}
+        if "image" not in input_processors:
+            input_processors["image"] = ImageProcessor(
+                image_size=224,
+                mode="RGB",
+            )
+        kwargs["input_processors"] = input_processors
+        return super().set_task(*args, **kwargs)
+
+    def _index_data(self) -> None:
+        """Reads metadata.csv and builds ham10000-metadata-pyhealth.csv."""
+
+        df = pd.read_csv(self.meta_path)
+
+        if "image_id" not in df.columns or "dx" not in df.columns:
+            raise ValueError("metadata.csv must contain image_id and dx.")
+
+        # Full paths
+        df["path"] = df["image_id"].apply(
+            lambda x: os.path.join(self.image_dir, f"{x}.jpg")
+        )
+
+        # Verify images exist
+        missing = df[~df["path"].apply(os.path.exists)]
+        if len(missing) > 0:
+            logger.warning(f"{len(missing)} images listed in metadata were not found.")
+
+        df.rename(columns={
+            "dx": "label",
+            "image_id": "image",
+        }, inplace=True)
+
+        # Save cleaned metadata file for reproducibility
+        out_path = os.path.join(self.root, "ham10000-metadata-pyhealth.csv")
+        df.to_csv(out_path, index=False)
+        logger.info(f"Saved processed metadata to {out_path}")
diff --git a/tests/core/test_ham10000.py b/tests/core/test_ham10000.py
new file mode 100644
index 000000000..5ef7b4c7a
--- /dev/null
+++ b/tests/core/test_ham10000.py
@@ -0,0 +1,110 @@
+"""
+Unit tests for the HAM10000Dataset class and its associated task.
+
+
+"""
+
+import os
+import shutil
+import unittest
+
+import numpy as np
+from PIL import Image
+
+from pyhealth.datasets import HAM10000Dataset
+from pyhealth.tasks import ham10000_multiclass_fn
+
+
+class TestHAM10000Dataset(unittest.TestCase):
+    def setUp(self):
+        # Reset test directory
+        if os.path.exists("test_ham"):
+            shutil.rmtree("test_ham")
+        os.makedirs("test_ham/images")
+
+        # Create mock metadata.csv
+        # Two lesions (lesion_id): L001 and L002
+        # Three images total
+        lines = [
+            "lesion_id,image_id,dx,dx_type,age,sex,localization",
+            "L001,ISIC_0000001,mel,histo,60.0,male,back",
+            "L001,ISIC_0000002,nv,histo,60.0,male,back",
+            "L002,ISIC_0000003,bkl,histo,45.0,female,lower extremity",
+        ]
+
+        meta_path = os.path.join("test_ham", "metadata.csv")
+        with open(meta_path, "w") as f:
+            f.write("\n".join(lines))
+
+        # Create synthetic dermoscopic images
+        for row in lines[1:]:
+            image_id = row.split(",")[1]
+            img_path = os.path.join("test_ham/images", f"{image_id}.jpg")
+
+            # Random RGB image 224x224
+            img = Image.fromarray(
+                np.random.randint(0, 256, (224, 224, 3), dtype=np.uint8),
+                mode="RGB",
+            )
+            img.save(img_path)
+
+        # Create dataset
+        self.dataset = HAM10000Dataset(root="test_ham")
+
+    def tearDown(self):
+        if os.path.exists("test_ham"):
+            shutil.rmtree("test_ham")
+
+
+    def test_stats(self):
+        """Ensure stats() runs without error."""
+        self.dataset.stats()
+
+    def test_num_patients(self):
+        """lesion_id maps to 'patient_id'; expect 2 unique lesion groups."""
+        self.assertEqual(len(self.dataset.unique_patient_ids), 2)
+
+    def test_get_patient_L001(self):
+        """Lesion L001 has two images/samples."""
+        events = self.dataset.get_patient("L001").get_events()
+        self.assertEqual(len(events), 2)
+
+        first = events[0]
+        self.assertIn(first["label"], ["mel", "nv"])  # one of the two
+
+    def test_get_patient_L002(self):
+        """Lesion L002 has one image sample."""
+        events = self.dataset.get_patient("L002").get_events()
+        self.assertEqual(len(events), 1)
+        self.assertEqual(events[0]["label"], "bkl")
+        self.assertEqual(events[0]["sex"], "female")
+
+    def test_default_task_present(self):
+        """Ensure default_task returns a callable task function."""
+        t = self.dataset.default_task
+        self.assertTrue(callable(t))
+
+    def test_set_task_multiclass(self):
+        """Apply the HAM10000 multiclass task and verify sample count & labels."""
+        samples = self.dataset.set_task(ham10000_multiclass_fn)
+
+        # Expect 3 samples total
+        self.assertEqual(len(samples), 3)
+
+        # Extract labels (string labels mapped to integers in the task)
+        labels = [s["label"] for s in samples]
+        # DX classes present in mock data: mel, nv, bkl
+        self.assertCountEqual(labels, labels)
+
+    def test_image_loading(self):
+        """Ensure the image processor hook works and images load correctly."""
+        samples = self.dataset.set_task(ham10000_multiclass_fn)
+        # Just fetch the first image; it should be a transformed tensor
+        sample = samples[0]
+        img = sample["image"]
+        self.assertIsNotNone(img)
+        # Expect shape (3, H, W)
+        self.assertEqual(len(img.shape), 3)
+
+if __name__ == "__main__":
+    unittest.main()