Wireless body area networks (WBANs) are crucial in healthcare technology for real-time monitoring and data transfer. However, their deployment faces challenges in energy efficiency and resource allocation. This paper presents an intelligent energy-efficient methodology for improving WBANs using machine learning algorithms to dynamically alter network parameters, reducing energy consumption and ensuring reliable data transmission. The optimization framework, which includes sensor data analytics, adaptive power management, and communication protocols, extends wearable device battery life. The model combines supervised learning for prediction and reinforcement learning for real-time decision-making, balancing energy efficiency and performance. Simulations and experiments show that the model can reduce energy consumption by up to 30% compared to conventional approaches without compromising service quality. The model also incorporates context-aware mechanisms that respond to user activities and environmental conditions, enhancing user experience. This adaptability enhances network stability and scalability, making it suitable for large-scale applications. The intelligent energy-efficient WBAN optimization model has the potential to advance wearable health monitoring devices, making them more reliable and sustainable for extended usage.