Dual-source heat pumps (DSHPs) represent a promising next-generation heating technology capable of overcoming the seasonal performance limitations of conventional air-source heat pumps (ASHPs), particularly the frosting and compressor inefficiencies experienced in cold climates. While DSHPs partially mitigate frost accumulation by mixing ambient and exhaust air streams, their performance remains highly dependent on dynamic thermal boundary conditions and the timing of defrost cycles. This paper proposes an advanced AI-enhanced predictive control framework for DSHPs that leverages machine learning (ML) to forecast frosting probability, optimize evaporator operating modes, and minimize compressor work. A thermodynamic model of the DSHP, including exergy destruction and entropy generation, is developed and integrated with a data-driven predictive control layer. Using experimentally validated datasets from prior DSHP research, the proposed controller reduces unnecessary defrost events, increases COP by 12–22% across varying ambient temperatures, and decreases compressor energy consumption by up to 18%. Exergy analysis demonstrates a reduction in irreversibility within the low-pressure evaporator during cold operation. The study concludes that predictive AI-driven optimisation offers a transformative pathway for DSHP performance, enabling significant energy savings, improved reliability, and broader adoption within smart low-carbon heat networks.