diff --git a/cpp/src/randomforest/randomforest.cuh b/cpp/src/randomforest/randomforest.cuh
index 356bddef4f..ba152dea27 100644
--- a/cpp/src/randomforest/randomforest.cuh
+++ b/cpp/src/randomforest/randomforest.cuh
@@ -7,6 +7,7 @@
 
 #include <cuml/ensemble/randomforest.hpp>
 
+#include <raft/core/device_setter.hpp>
 #include <raft/core/handle.hpp>
 #include <raft/core/nvtx.hpp>
 #include <raft/random/permute.cuh>
@@ -120,9 +121,11 @@ class RandomForest {
            bool* bootstrap_masks = nullptr)
   {
     raft::common::nvtx::range fun_scope("RandomForest::fit @randomforest.cuh");
-    this->error_checking(input, labels, n_rows, n_cols, false);
     const raft::handle_t& handle = user_handle;
-    int n_sampled_rows           = 0;
+    int handle_device            = handle.get_device();
+    auto device_guard            = raft::device_setter(handle_device);
+    this->error_checking(input, labels, n_rows, n_cols, false);
+    int n_sampled_rows = 0;
     if (this->rf_params.bootstrap) {
       n_sampled_rows = std::round(this->rf_params.max_samples * n_rows);
     } else {
@@ -162,8 +165,9 @@ class RandomForest {
 
 #pragma omp parallel for num_threads(n_streams)
     for (int i = 0; i < this->rf_params.n_trees; i++) {
-      int stream_id = omp_get_thread_num();
-      auto s        = handle.get_stream_from_stream_pool(stream_id);
+      auto thread_device_guard = raft::device_setter(handle_device);
+      int stream_id            = omp_get_thread_num();
+      auto s                   = handle.get_stream_from_stream_pool(stream_id);
 
       this->get_row_sample(i, n_rows, &selected_rows[stream_id], s);
 
diff --git a/python/cuml/tests/test_fil.py b/python/cuml/tests/test_fil.py
index 0997074f48..429fde355b 100644
--- a/python/cuml/tests/test_fil.py
+++ b/python/cuml/tests/test_fil.py
@@ -935,13 +935,10 @@ def test_device_selection(device_id, model_kind, tmp_path):
         )
 
         with device_context:
-            # TODO(hcho3): Remove n_streams=1 argument once the bug
-            # https://github.com/rapidsai/cuml/issues/5983 is resolved
             cuml_model = cumlRandomForestClassifier(
                 max_depth=3,
                 random_state=0,
                 n_estimators=n_estimators,
-                n_streams=1,
             )
             cuml_model.fit(cp.array(X), cp.array(y))
             fm = cuml_model.as_fil()
diff --git a/python/cuml/tests/test_random_forest.py b/python/cuml/tests/test_random_forest.py
index 73e15403c0..1501ce3c98 100644
--- a/python/cuml/tests/test_random_forest.py
+++ b/python/cuml/tests/test_random_forest.py
@@ -9,6 +9,7 @@
 import warnings
 
 import cudf
+import cupy as cp
 import numpy as np
 import pytest
 import treelite
@@ -1046,6 +1047,36 @@ def test_rf_predict_returns_int():
     assert pred.dtype == np.int64
 
 
+@pytest.mark.skipif(
+    cp.cuda.runtime.getDeviceCount() < 2,
+    reason="test requires at least two visible CUDA devices",
+)
+def test_rf_fit_with_cupy_nondefault_device():
+    # Verify that cuml operations respect the current device context and
+    # restore it upon completion. This test checks that cuML correctly saves
+    # and restores the CUDA device ID, ensuring it does not inadvertently
+    # modify the device set by external code.
+
+    # IMPORTANT: This test does **not** document or endorse ``cp.cuda.Device``
+    # (or ``cp.cuda.runtime.setDevice``) as a supported API for switching
+    # devices in cuML. Switching devices via CuPy is generally **not
+    # supported**.
+    current_device = cp.cuda.runtime.getDevice()
+
+    with cp.cuda.Device(1):
+        X = cp.random.normal(size=(10, 4)).astype(cp.float32)
+        y = cp.asarray([0, 1] * 5, dtype=cp.int32)
+
+        clf = cuml.ensemble.RandomForestClassifier(
+            max_features=1.0,
+            n_bins=8,
+            n_estimators=2,
+        )
+        clf.fit(X, y)
+
+    assert cp.cuda.runtime.getDevice() == current_device
+
+
 def test_ensemble_estimator_length():
     X, y = make_classification()
     clf = cuml.ensemble.RandomForestClassifier(n_estimators=3)