Accurate parameter extraction in photovoltaic (PV) cells is essential for optimizing the performance and reliability of solar energy systems. This paper introduces the Black-winged Kite Algorithm (BKA), a novel metaheuristic optimization technique inspired by the predatory and migratory behaviors of black-winged kites. By integrating the Cauchy mutation strategy and a dynamic leadership mechanism, the BKA achieves a fine balance between global exploration and local exploitation, enabling it to efficiently navigate complex search spaces and avoid local optima. The proposed algorithm is applied to the parameter extraction of PV cells modeled using the Single Diode Model (SDM) and evaluated against state-of-the-art metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), and execution time. Simulations performed using MATLAB demonstrate the BKA’s ability to deliver superior accuracy and computational efficiency compared to existing algorithms. This study establishes the BKA as a robust and efficient tool for addressing the challenges of PV parameter extraction, paving the way for enhanced modeling and optimization in renewable energy systems.