Personalized workout guidance is essential for achieving effective and safe fitness outcomes. Most existing fitness applications provide generic workout plans without adequately considering individual body parameters. This paper proposes a Workout Recommendation System that suggests personalized workouts based on user attributes such as Body Mass Index (BMI), age, and gender. The system classifies users into different fitness categories and recommends suitable exercises using machine learning techniques. The proposed approach aims to improve workout effectiveness, reduce the risk of injuries, and promote healthier lifestyles. The system follows a structured pipeline involving data collection, preprocessing, and recommendation generation. Future enhancements may include wearable device integration and diet-based recommendations.