The rapid growth of wearable healthcare devices, implantable sensors, and IoT networks has created a need for smart wireless systems that can operate independently with minimal battery dependence. This paper presents a theoretical study of an AI-enabled hybrid nanomaterial smart antenna integrated with multi-source energy harvesting for autonomous biomedical and IoT applications. The proposed design utilises advanced nanomaterials such as graphene, MXene, carbon nanotubes (CNTs), and flexible polymer composites to achieve lightweight, flexible, and high-performance antenna structures. To enable self-powered operation, the system harvests energy from ambient RF signals, body heat, and sunlight. Artificial intelligence is employed to dynamically optimise antenna characteristics, including frequency, beam direction, polarisation, impedance matching, and power management based on changing environmental conditions. Theoretical analysis indicates that combining AI, hybrid nanomaterials, and multi-source energy harvesting can enhance communication reliability, energy efficiency, adaptability, and device lifetime. The future approach offers a promising solution for next-generation wearable healthcare systems, implantable medical devices, smart cities, Industry 5.0, and autonomous IoT networks.