diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index 930177103b0a6..d6bdc1c715af0 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -10,7 +10,6 @@
 from numbers import Integral, Real
 
 import numpy as np
-from joblib import effective_n_jobs
 from scipy import optimize
 
 from sklearn._loss.loss import HalfBinomialLoss, HalfMultinomialLoss
@@ -894,7 +893,10 @@ class of problems.
            *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.
 
     n_jobs : int, default=None
-        Not used at the moment.
+        Does not have any effect.
+
+        .. deprecated:: 1.8
+           `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.
 
     l1_ratio : float, default=None
         The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
@@ -1090,6 +1092,13 @@ def fit(self, X, y, sample_weight=None):
                 "(penalty={})".format(self.penalty)
             )
 
+        msg = (
+            "'n_jobs' has no effect since 1.8 and will be removed in 1.10. "
+            f"You provided 'n_jobs={self.n_jobs}', please leave it unspecified."
+        )
+        if self.n_jobs is not None:
+            warnings.warn(msg, category=FutureWarning)
+
         if self.penalty == "elasticnet" and self.l1_ratio is None:
             raise ValueError("l1_ratio must be specified when penalty is elasticnet.")
 
@@ -1139,12 +1148,6 @@ def fit(self, X, y, sample_weight=None):
                     "value > 1e30 results in a frozen fit. Please choose another "
                     "solver or rescale the input X."
                 )
-            if effective_n_jobs(self.n_jobs) != 1:
-                warnings.warn(
-                    "'n_jobs' > 1 does not have any effect when"
-                    " 'solver' is set to 'liblinear'. Got 'n_jobs'"
-                    " = {}.".format(effective_n_jobs(self.n_jobs))
-                )
             self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
                 X,
                 y,
@@ -1183,8 +1186,8 @@ def fit(self, X, y, sample_weight=None):
                 warm_start_coef, self.intercept_[:, np.newaxis], axis=1
             )
 
-        # TODO: deprecate n_jobs since it's not used anymore and enable multi-threading
-        # if benchmarks show a positive effect.
+        # TODO: enable multi-threading if benchmarks show a positive effect,
+        # see https://github.com/scikit-learn/scikit-learn/issues/32162
         n_threads = 1
 
         coefs, _, n_iter = _logistic_regression_path(
diff --git a/sklearn/linear_model/tests/test_logistic.py b/sklearn/linear_model/tests/test_logistic.py
index 0a064658582fa..3c8b81fb62a15 100644
--- a/sklearn/linear_model/tests/test_logistic.py
+++ b/sklearn/linear_model/tests/test_logistic.py
@@ -35,7 +35,7 @@
 from sklearn.preprocessing import LabelEncoder, StandardScaler, scale
 from sklearn.svm import l1_min_c
 from sklearn.utils import compute_class_weight, shuffle
-from sklearn.utils._testing import ignore_warnings, skip_if_no_parallel
+from sklearn.utils._testing import ignore_warnings
 from sklearn.utils.fixes import _IS_32BIT, COO_CONTAINERS, CSR_CONTAINERS
 
 pytestmark = pytest.mark.filterwarnings(
@@ -119,20 +119,6 @@ def __call__(self, model, X, y, sample_weight=None):
     assert mock_scorer.calls == 1
 
 
-@skip_if_no_parallel
-def test_lr_liblinear_warning():
-    X, y = make_classification(random_state=0)
-
-    lr = LogisticRegression(solver="liblinear", n_jobs=2)
-    warning_message = (
-        "'n_jobs' > 1 does not have any effect when"
-        " 'solver' is set to 'liblinear'. Got 'n_jobs'"
-        " = 2."
-    )
-    with pytest.warns(UserWarning, match=warning_message):
-        lr.fit(X, y)
-
-
 @pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
 def test_predict_3_classes(csr_container):
     check_predictions(LogisticRegression(C=10), X, Y2)
@@ -2619,3 +2605,12 @@ def test_logisticregressioncv_warns_with_use_legacy_attributes():
     msg = "The default value of use_legacy_attributes will change from True"
     with pytest.warns(FutureWarning, match=msg):
         lr.fit(X, y)
+
+
+# TODO(1.10): remove this test when n_jobs gets removed
+def test_logisticregression_warns_with_n_jobs():
+    X, y = make_classification(n_classes=3, n_samples=50, n_informative=6)
+    lr = LogisticRegression(n_jobs=1)
+    msg = "'n_jobs' has no effect"
+    with pytest.warns(FutureWarning, match=msg):
+        lr.fit(X, y)