In Wireless Body Area Networks (WBAN), ensuring reliable and continuous monitoring of physiological signals is crucial, yet WBANs are prone to faults that can reduce accuracy. This paper introduces a hybrid approach to fault prediction that combines ensemble Artificial Intelligence (AI) techniques with Explainable AI (XAI) and Data Visualization to enhance accuracy, reliability, and interpretability. The ensemble approach, which integrates eight machine learning techniques, was selected to improve robustness and manage diverse data from multiple sensors. By leveraging the strengths of individual algorithms, the ensemble model captures complex patterns and interactions within sensor data, reducing the impact of sensor noise, single-sensor failures, and misclassifications. This integration not only increases fault prediction accuracy by more than 10% over traditional methods but also enhances the model’s adaptability to heterogeneous data. To enhance model transparency, techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are utilized. These methods empower healthcare practitioners to decipher the impact of individual variables on predictive outcomes. SHAP, in particular, assigns an attribution score to each feature, illustrating its role in fault detection, while LIME provides interpretable, locally focused explanations for each prediction. This interpretability is essential for healthcare professionals, as it helps them understand model reasoning, facilitating more informed decisions and better patient outcomes. Additionally, data visualization enhances patient care and supports proactive decision-making by allowing healthcare providers to monitor predictive patterns. Comprehensive evaluations demonstrate the exceptional efficacy of the proposed framework, highlighting its advantages in precision, latency optimization, and resource utilization, making it a promising solution for improved fault tolerance in WBANs and underscoring the potential for more autonomous and intelligent solutions.