Tennis forehand is a tricky activity that employs practically all parts of a body. The legs and the trunk are important as well as the arm and the wrist, as they determine the speed with which the racket attains movement, and influence the mastery that the player is going to have over the ball when it strikes and is entered. Surface electromyography (sEMG) is a non-invasive method of recording muscle switching on and the intensity of the work. Kinematic and wearable-sensor data provide an addition of a picture of the joint motion in the body segments. This article gathers previously known biomechanical evidence of the forehand as well as extending it into a technology-centered paradigm of AI-assisted coaching. Recent studies indicate that not all players who activate their muscles more are necessarily stronger. It seems to be their benefit of a superior timing and improved alignment of the segments, as well as the accelerated movement of the swing. The suggested paradigm begins with the combination of sEMG and inertial or kinematic measurements. Signals are initially cleansed. Attributes are subsequently obtained and fed through machine-learning algorithms that identify movement patterns. The last steps are error identification and personalized coaching feedback. What this might imply to practical teaching (core-centered movement instruction and control of excessive co-contraction) is also given due consideration in the article. The concepts of the wrist and forearm control and ground reaction force awareness are also discussed. The framework is theoretical and not clinically or performance validated. Annotated multimodal data sets should be built in future work and contrasting machine-learning models should be compared. It will also need to evaluate their generalization between players and skill levels, and whether AI-generated feedback does indeed positively affect the measurable tennis results.