diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
index d5746a38582c..c6f3b47419cd 100644
--- a/python/tvm/relay/frontend/tensorflow.py
+++ b/python/tvm/relay/frontend/tensorflow.py
@@ -926,13 +926,6 @@ def _impl(inputs, attr, params, mod):
 
         data = inputs[3]
 
-        # By default, in tensorflow the first input ,i.e., data is sparse
-        sparse_lhs = True
-
-        # If both are true means First input was dense and second was sparse
-        if attr.get("adjoint_a") and attr.get("adjoint_b"):
-            sparse_lhs = False
-
         rows = [x[0] for x in indices_tensor]
         cols = [x[1] for x in indices_tensor]
 
@@ -941,9 +934,53 @@ def _impl(inputs, attr, params, mod):
             (values_tensor, (rows, cols)), shape=tuple(dense_shape_tensor.tolist())
         )
 
-        if sparse_lhs:
+        # As per tensorflow implementation, we have 4 possible input combination
+        # and the first input(A) is always sparse and second input(B) is always dense.
+        # Case 1: A , B , adjoint_a=False, adjoint_b=False  --> A * B
+        # Case 2: A , B , adjoint_a=True,   adjoint_b=False  --> A.T * B
+        # Case 3: A , B , adjoint_a=False, adjoint_b=True    --> A * B.T
+        # Case 4: A , B , adjoint_a=True,   adjoint_b=True    --> A.T * B.T
+        #
+        # Topi implementation for sparse_dense(matmul) has 2 possible input
+        # combination where first input(A) is always dense
+        # and second input(B) is always sparse.
+        # Case 1: A , B, sparse_lhs = False  --> A * B.T
+        # Case 2: A , B, sparse_lhs = True    --> B * A.T
+        #
+        # The mapping would be as below:
+        # TF Case 1: A , B , adjoint_a=False, adjoint_b=False
+        #           --> In TF: A * B   --> In Topi: A * B.T.T
+        #           --> sparse_dense(transpose(B), A, sparse_lhs=True)
+        #
+        # TF Case 2: A , B , adjoint_a=True, adjoint_b=False
+        #           --> In TF: A.T * B   --> In Topi: A.T * B.T.T
+        #           --> sparse_dense(transpose(B), transpose(A), sparse_lhs=True)
+        #
+        # TF Case 3: A , B , adjoint_a=False, adjoint_b=True
+        #           --> In TF: A * B.T   --> In Topi: A * B
+        #           --> sparse_dense(B, A, sparse_lhs=True)
+        #
+        # TF Case 4: A , B , adjoint_a=True, adjoint_b=True
+        #           --> In TF: A.T * B.T   --> In Topi: (B * A.T).T
+        #           --> transpose(sparse_dense(B, transpose(A), sparse_lhs=False))
+
+        # By default, in tensorflow the first input ,i.e., data is sparse
+        sparse_lhs = True
+
+        # TF Case 1:
+        if not attr.get("adjoint_a") and not attr.get("adjoint_b"):
+            data = _op.transpose(data)
+        # TF Case 2:
+        elif attr.get("adjoint_a") and not attr.get("adjoint_b"):
             data = _op.transpose(data)
+            weight_sp = csr_matrix(weight_sp.transpose())
+        # TF Case 3:
+        elif not attr.get("adjoint_a") and attr.get("adjoint_b"):
+            pass
+        # TF Case 4:
+        # attr.get("adjoint_a") and attr.get("adjoint_b"):
         else:
+            sparse_lhs = False
             weight_sp = csr_matrix(weight_sp.transpose())
 
         weight_data = _expr.const(weight_sp.data, weight_sp.data.dtype)
@@ -953,23 +990,9 @@ def _impl(inputs, attr, params, mod):
         ret = _op.nn.sparse_dense(data, [weight_data, weight_indices, weight_indptrs], sparse_lhs)
 
         if not sparse_lhs:
+            # TF Case 4
             ret = _op.transpose(ret)
 
-        # Case 1. If both are true means first input was dense and second was sparse
-        # Case 2. If both are false means first input was sparse and second was dense
-        # TODO(ANSHUMAN87): Support other adjoint option too
-        if not (
-            (attr.get("adjoint_a") and attr.get("adjoint_b"))
-            or ((not attr.get("adjoint_a")) and (not attr.get("adjoint_b")))
-        ):
-            raise tvm.error.OpAttributeUnImplemented(
-                "Only tf.sparse.sparse_dense_matmul() with adjoint_a=True and adjoint_b=True"
-                "or with adjoint_a=False and adjoint_b=False"
-                " is supported, but adjoint_a={} and adjoint_b={} was supplied.".format(
-                    attr.get("adjoint_a"), attr.get("adjoint_b")
-                )
-            )
-
         return ret
 
     return _impl
diff --git a/tests/python/frontend/tensorflow/test_forward.py b/tests/python/frontend/tensorflow/test_forward.py
index d71405796ede..ccd17a4fab85 100644
--- a/tests/python/frontend/tensorflow/test_forward.py
+++ b/tests/python/frontend/tensorflow/test_forward.py
@@ -1758,19 +1758,21 @@ def test_forward_batch_matmul():
 # ----------------------------------
 
 
-def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype, flip=False):
+def _test_sparse_dense_matmul(indices, values, A_inp_shape, B_inp_shape, dtype, flip=False):
     """ One iteration of sparse_dense_matmul """
 
-    # TODO(ANSHUMAN87): Support adjoint options too
-    for adjoint_a in [False]:
-        for adjoint_b in [False]:
+    for adjoint_a in [False, True]:
+        for adjoint_b in [False, True]:
+            A_shape = A_inp_shape[::-1] if adjoint_a else A_inp_shape
+            B_shape = B_inp_shape[::-1] if adjoint_b else B_inp_shape
+
             with tf.Graph().as_default():
                 A_sp = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=A_shape)
                 B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")
 
                 if flip:
                     result = tf.sparse.sparse_dense_matmul(
-                        B, A_sp, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+                        B, A_sp, adjoint_a=adjoint_b, adjoint_b=adjoint_a
                     )
                 else:
                     result = tf.sparse.sparse_dense_matmul(