diff --git a/daal4py/sklearn/_n_jobs_support.py b/daal4py/sklearn/_n_jobs_support.py
index 78d9cf0208..74fe707825 100644
--- a/daal4py/sklearn/_n_jobs_support.py
+++ b/daal4py/sklearn/_n_jobs_support.py
@@ -19,11 +19,11 @@
 import threading
 from functools import wraps
 from inspect import Parameter, signature
-from multiprocessing import cpu_count
 from numbers import Integral
 from warnings import warn
 
 import threadpoolctl
+from joblib import cpu_count
 
 from daal4py import daalinit as set_n_threads
 from daal4py import num_threads as get_n_threads
@@ -46,24 +46,26 @@ def get_suggested_n_threads(n_cpus):
     Usually, limit is equal to `n_logical_cpus` // `n_jobs`.
     Returns None if limit is not set.
     """
-    n_threads_map = {
-        lib_ctl.internal_api: lib_ctl.get_num_threads()
-        for lib_ctl in threadpool_controller.lib_controllers
-        if lib_ctl.internal_api != "mkl"
-    }
-    # openBLAS is limited to 24, 64 or 128 threads by default
-    # depending on SW/HW configuration.
-    # thus, these numbers of threads from openBLAS are uninformative
-    if "openblas" in n_threads_map and n_threads_map["openblas"] in [24, 64, 128]:
-        del n_threads_map["openblas"]
-    # remove default values equal to n_cpus as uninformative
-    for backend in list(n_threads_map.keys()):
-        if n_threads_map[backend] == n_cpus:
-            del n_threads_map[backend]
-    if len(n_threads_map) > 0:
-        return min(n_threads_map.values())
-    else:
-        return None
+
+    # Comment 2025-11-18: as of joblib>=1.5.2, by the point that this section
+    # is reached under a joblib job (e.g. as triggered by sklearn metaestimators)
+    # or under a threadpoolctl context that doesn't specify 'api', limits for
+    # openmp will always be set - in the case of joblib, to the number of threads
+    # divided by the number of parallel jobs, and in the case of threadpoolctl,
+    # to the number that is passed under the context. However, limits for other
+    # components like 'openblas' would be set under some setups but not others
+    # (e.g. if installing SciPy from pip with its bundled openblas, but not if
+    # installing it from conda-forge with MKL as BLAS backend), and might be set
+    # by the user to something that doesn't match with the number of parallel
+    # jobs from joblib - hence this looks at the openmp configuration, even
+    # though openmp is not used by oneDAL.
+    for lib_ctl in threadpool_controller.lib_controllers:
+        if lib_ctl.internal_api == "openmp":
+            n_threads = lib_ctl.get_num_threads()
+            # remove default values equal to n_cpus as uninformative
+            if n_threads is not None and n_threads != n_cpus:
+                return n_threads
+    return None
 
 
 def _run_with_n_jobs(method):