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The viral increase in the Internet of Things (IoT) has caused previously unimaginable difficulties with ensuring the sustainability of the energy supply of sensor nodes particularly within remote and energy-limited conditions. This paper describes a new Smart-Edge RF Harvesting and Reconfigurable Antenna Array system that is proposed to provide energy-independent and context-aware IoT communications. The proposed system combines a wideband RF energy harvester circuit that has been optimized using ADS, a reconfigurable MEMS-based antenna array that is simulated in HFSS, and a low-power microcontroller platform that includes TinyML-based situational adaptation intelligence. This system is able to capture ambient RF energy at frequencies between 900 MHz, 2.4 GHz and 5.8 GHz with a highest power conversion efficiency of 78.3% at +5 dBm input. The antenna module showed accurate steering of the beam with the maximum directional gain of 7.9 dBi and the mean return loss of -14 dB. Transmission protocols that are energy-aware adjust the duty cycle and power level to real-time residual energy and can achieve more than 95% packet delivery even at low energy thresholds. The inference of TinyML was able to attain an environmental RF zone classification with 96.4% accuracy and a latency of sub-15 ms. Comparative benchmarking shows that there are great performance gains in the energy sustainability, communication range and smart adaptability against the conventional systems. The integrated solution is a new move towards self-sustaining, smart IoT infrastructure, which is applicable in smart agriculture, city-wide surveillance, and disaster management. RF energy harvesting, an adaptive antenna system, and edge AI, combine to provide a powerful platform of next-generation IoT deployments in the power-scarce environments.