Abstract: Continuous physiological monitoring using wearable and intelligent healthcare devices has increased the need for dependable data processing with lower computational demands and power consumption. Physiological conditions can be evaluated using biosignals such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG). However, the continuous acquisition and analysis of these signals present challenges related to computational complexity, power consumption, and data communication. Combining energy- and entropy-based signal features with machine learning and explainable artificial intelligence can improve the efficiency and reliability of healthcare decision-support systems. Applications such as EEG motor imagery classification, automated sleep stage recognition, and predictive healthcare analytics can benefit from effective feature selection and computationally efficient learning models to interpret physiological signals. Adaptive signal processing combined with edge intelligence can also enable real-time analysis, reducing the need for cloud-based computation and excessive data transmission. Balancing predictive accuracy, computational efficiency, energy consumption, and model interpretability remains a challenge in resource-constrained healthcare applications. Advances in low-power sensing, intelligent feature extraction, and energy-aware edge architectures can support the development of sustainable, responsive, and reliable healthcare monitoring systems.