how to tune hyperparameters inn regression neural network

hope you are enjoying good health,i am trying to built a simple neural network which has to predict a shear wave well log values from other well logs,but my model's is stuck in mean absolute error of 2.45 and it is not improving further,i have changed the number of neurons,learning rate,loss function but of no use.

Here is my model:

tf.random.set_seed(42)
model=tf.keras.Sequential([
    tf.keras.layers.Dense(22,activation='relu'),
    tf.keras.layers.Dense(1)
])

#commpiling:    
model.compile(
    loss=tf.losses.mae,
    optimizer=tf.optimizers.Adam(learning_rate=0.006),
    metrics=['mae']
)

#fitting:
history=model.fit(x_train,y_train,epochs=1000,verbose=0,)

#evaluation:
model.evaluate(x_test,y_test)

here is the boxplot of my input data,data shape is (1700,7)

here is the loss graph: