This paper 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 critical for continuous health monitoring, but they face challenges such as high energy consumption and sensor overheating. The proposed method uses dynamic thresholds to optimize sensor activity, extending battery life and maintaining optimal temperatures. The study utilizes the MIT-BIH Arrhythmia dataset to improve arrhythmia detection, achieving a 98% classification accuracy for ECG signals. The paper discusses the challenges of WBANs, including energy efficiency and accurate health monitoring. It reviews existing research on WBANs, highlighting the use of deep learning and other techniques to enhance performance. The proposed method introduces a dynamic threshold mechanism that allows sensors to sleep during normal heart rates, conserving energy. This mechanism is combined with CNNs to reduce false positives and ensure accurate monitoring. The methodology involves data preprocessing, resampling, and the use of a dynamic threshold mechanism. The CNN model is trained on the MIT-BIH Arrhythmia dataset, achieving high classification accuracy. The results show a 10.53% improvement in battery life and a 5.62-fold enhancement in temperature management when sleep mode technology is applied. The model achieves high accuracy in detecting normal heartbeats and satisfactory results in classifying arrhythmias. The experimental results demonstrate the effectiveness of the proposed method in improving battery life and temperature management. The simulation results show that sensors with sleep mode exhibit better performance in terms of battery life and temperature regulation. The optimization ratios for battery life and temperature management are calculated, showing significant improvements with the proposed method. The study concludes that the proposed method enhances energy efficiency and thermal management in WBANs, making them more viable for continuous health monitoring.This paper 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 critical for continuous health monitoring, but they face challenges such as high energy consumption and sensor overheating. The proposed method uses dynamic thresholds to optimize sensor activity, extending battery life and maintaining optimal temperatures. The study utilizes the MIT-BIH Arrhythmia dataset to improve arrhythmia detection, achieving a 98% classification accuracy for ECG signals. The paper discusses the challenges of WBANs, including energy efficiency and accurate health monitoring. It reviews existing research on WBANs, highlighting the use of deep learning and other techniques to enhance performance. The proposed method introduces a dynamic threshold mechanism that allows sensors to sleep during normal heart rates, conserving energy. This mechanism is combined with CNNs to reduce false positives and ensure accurate monitoring. The methodology involves data preprocessing, resampling, and the use of a dynamic threshold mechanism. The CNN model is trained on the MIT-BIH Arrhythmia dataset, achieving high classification accuracy. The results show a 10.53% improvement in battery life and a 5.62-fold enhancement in temperature management when sleep mode technology is applied. The model achieves high accuracy in detecting normal heartbeats and satisfactory results in classifying arrhythmias. The experimental results demonstrate the effectiveness of the proposed method in improving battery life and temperature management. The simulation results show that sensors with sleep mode exhibit better performance in terms of battery life and temperature regulation. The optimization ratios for battery life and temperature management are calculated, showing significant improvements with the proposed method. The study concludes that the proposed method enhances energy efficiency and thermal management in WBANs, making them more viable for continuous health monitoring.