This article presents an enhancement for Wireless Body Area Networks (WBANs) using adaptive algorithms that combine Convolutional Neural Networks (CNNs) with dynamic threshold mechanisms to improve performance and energy efficiency. WBANs are essential for continuous health monitoring, but they face challenges such as high energy consumption and sensor overheating. The proposed method addresses these issues by utilizing dynamic thresholds and CNNs to optimize sensor activity, thereby extending battery life and maintaining optimal sensor temperatures. The study uses the MIT-BIH Arrhythmia dataset to improve the detection of arrhythmias, achieving a 98% classification accuracy. The paper discusses various related works that have explored methods to enhance WBAN performance, including energy-efficient routing techniques, duty cycle optimization for energy-harvesting nodes, and MAC protocols for reducing energy consumption. These studies highlight the importance of energy efficiency and accurate health monitoring in WBANs. The methodology involves data preprocessing, resampling, and dynamic thresholding to ensure accurate and efficient monitoring. The proposed CNN model processes ECG signals, reducing false positives and ensuring accurate classification of arrhythmias. The model was trained on the MIT-BIH Arrhythmia dataset, achieving high classification accuracy. The experimental results show that the proposed method significantly improves battery life and temperature management. Sensors with sleep mode technology demonstrated a 10.53% improvement in battery life and a 5.62-fold enhancement in temperature management compared to sensors without sleep mode. The results indicate that the adaptive approach effectively conserves energy and ensures accurate health monitoring. The study concludes that the proposed method enhances energy efficiency and thermal management in WBANs, making them viable for continuous health monitoring. The use of dynamic thresholds and CNNs in WBANs provides a promising solution for improving the performance and reliability of these networks in healthcare applications.This article presents an enhancement for Wireless Body Area Networks (WBANs) using adaptive algorithms that combine Convolutional Neural Networks (CNNs) with dynamic threshold mechanisms to improve performance and energy efficiency. WBANs are essential for continuous health monitoring, but they face challenges such as high energy consumption and sensor overheating. The proposed method addresses these issues by utilizing dynamic thresholds and CNNs to optimize sensor activity, thereby extending battery life and maintaining optimal sensor temperatures. The study uses the MIT-BIH Arrhythmia dataset to improve the detection of arrhythmias, achieving a 98% classification accuracy. The paper discusses various related works that have explored methods to enhance WBAN performance, including energy-efficient routing techniques, duty cycle optimization for energy-harvesting nodes, and MAC protocols for reducing energy consumption. These studies highlight the importance of energy efficiency and accurate health monitoring in WBANs. The methodology involves data preprocessing, resampling, and dynamic thresholding to ensure accurate and efficient monitoring. The proposed CNN model processes ECG signals, reducing false positives and ensuring accurate classification of arrhythmias. The model was trained on the MIT-BIH Arrhythmia dataset, achieving high classification accuracy. The experimental results show that the proposed method significantly improves battery life and temperature management. Sensors with sleep mode technology demonstrated a 10.53% improvement in battery life and a 5.62-fold enhancement in temperature management compared to sensors without sleep mode. The results indicate that the adaptive approach effectively conserves energy and ensures accurate health monitoring. The study concludes that the proposed method enhances energy efficiency and thermal management in WBANs, making them viable for continuous health monitoring. The use of dynamic thresholds and CNNs in WBANs provides a promising solution for improving the performance and reliability of these networks in healthcare applications.