This research introduces a novel method to enhance the stability and control of a bipedal robot by optimizing servo angles using a TensorFlow-based model. Traditionally, bipedal robots have relied on kinematic and mechanical approaches to achieve stability. However, these methods are often affected by servo mechanism errors, leading to unexpected and inaccurate movements. Through a comprehensive comparison of robot motions before and after integrating TensorFlow, we demonstrate that the model predicts smoother and more stable servo angles by leveraging TensorFlow’s advanced deep learning capabilities. This research presents an AI-driven technique that enhances servo movements and reduces errors without the need for costly, sophisticated controllers. Positional data from potentiometers were used to train the model, capturing a range of joint angles from each servo. Experimental results highlight the efficiency of this AI-powered approach in enhancing robotic locomotion, demonstrating a significant improvement in gait stability.