A unique multi-modal energy harvesting architecture for sustainable, long-term autonomous wearable electronics operation, especially in distant or infrastructure-deficient locations, is presented here. RF scavenging, thermoelectric generators (TEGs), piezoelectric energy harvesters, flexible photovoltaic (PV) modules, and ergonomic, wearable platform are unique to the suggested architecture. A lightweight, context-aware energy management unit (EMU) using a decision tree-based machine learning model for real-time, predictive source selection is a major breakthrough. In extensive real-world field testing in mountain hiking, tropical fieldwork, urban roaming, and remote healthcare, the proposed system reduced external charging needs by up to 85%, achieved over 90% device uptime, and achieved 93.4% real-time source selection accuracy. Our architecture dynamically changes and combines numerous sources to offer resilient power in response to varied environmental and physiological inputs, unlike previous systems. This platform offers the groundwork for sustainable, self-sufficient, and intelligent wearable gadgets that can withstand harsh conditions by reducing battery and charger use. © 2025 Elsevier B.V., All rights reserved.