This study presents several brushless DC (BLDC) motor drive topologies suitable for solar photovoltaic (PV) arrayfed air conditioning systems employing an adaptive constantvoltage maximum power point tracking (MPPT) algorithm. To enhance system compactness and reduce overall cost, a novel single-stage PV-fed BLDC motor drive configuration is proposed, which eliminates the need for an intermediate DC-DC converter. Additionally, sensor-less control strategies are developed for both single-stage and two-stage configurations to further minimize cost and improve system integration. Sensor-less operation, being highly suitable for air conditioning systems, is given particular emphasis due to its reliability and simplicity. A comprehensive performance evaluation is conducted based on efficiency, cost-effectiveness, design simplicity, and dynamic response. The proposed adaptive constant-voltage MPPT algorithm enables rapid and stable power tracking under varying irradiance and temperature conditions. Simulation and experimental results obtained from a laboratory-developed hardware prototype confirm the practicality and effectiveness of the proposed approach, demonstrating its strong potential for realworld solar-powered air conditioning applications. In the future, AI-driven predictive and self-learning algorithms can further enhance PV energy utilization, enabling autonomous optimization, fault diagnosis, and smart-grid integration for nextgeneration sustainable energy systems. simulation of an Artificial Intelligence (AI)–based Adaptive Constant Voltage Maximum Power Point Tracking (MPPT) algorithm for a photovoltaic (PV)- fed Brushless DC (BLDC) motor drive used in air conditioning applications. techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic to dynamically estimate the optimal operating voltage of the PV array under varying irradiance and temperature conditions. By directly coupling the PV array to the voltage source inverter (VSI) and using AI-assisted adaptive control, the system achieves faster convergence, minimal steadystate oscillations, and higher energy conversion efficiency compared to traditional MPPT methods