A Sensor-Integrated Smart Tennis racket is introduced that will be able to provide real-time analysis of a player’s motion and thereby be able to improve player performance. The conventional coaching method is dependent on post-game corrective measures as against the time of the game. Therefore, our system integrates two MPU-6050 motion sensors, one mounted to the wrist and the other to the racket, for the recording of motion data. An ESP32-S3 Mini Development Board connects as a wireless transmission device between the sensors and processes the captured input using Long Short-Term Memory (LSTM) neural networks for classifying different shots of tennis, including forehand, backhand, smash, and half-volley shots. Based on the data collected from this study, the designed LSTM model proved a remarkable 97.22% accuracy in distinguishing between the shots to enable precise biomechanical analysis. In controlled experiments, it was shown that training with the developed real-time feedback system improved performance by about 20% compared to traditional training. The coach can detect inconsistency in technique from the system and bring in timely feedback, which will fast-track the building of skills. Thus, there is a solution to this where the player will use a smart racket for improving performance because it will help track and modify the process using the integrated sensor technology and machine learning-based approach. The research sets the very foundation for future advancements in AI-assisted sports training, leading to much broader application in other racket-based sports.