Shadowing in photovoltaic (PV) systems often causes significant reductions in power generation. Traditional maximum power point tracking (MPPT) techniques, such as Perturb and Observe and Incremental Conductance, struggle to identify the global peak in the power-voltage (P-V) curve, particularly under partial shading, due to limitations such as longer tracking times and steady-state oscillations. To address these challenges, optimization-based algorithms have been employed for more accurate MPPT. While Particle Swarm Optimization (PSO) has demonstrated effectiveness in this domain, it suffers from high transient fluctuations, poor convergence, and excessive computational time due to intensive parameter involvement and randomness. This study introduces the Magnificent Frigatebird Optimization (MFO) technique, designed to enhance MPPT performance and to reduce the computational effort. The magnificent frigatebird is known for its remarkable soaring ability, quick maneuverability, and energy-efficient hunting techniques, enabling it to fly long distances with minimal effort. Extracting these features, the proposed method is tested on solar PV systems rated at 240 W and 480 W under dynamic irradiance conditions. Extensive numerical simulations and PV emulator investigations are conducted under various shadowing scenarios. A detailed comparative analysis shows that the proposed MFO method achieves faster settling times—40% better than Grey Wolf Optimization, 30% better than Particle Swarm Optimization, 10% better than the Bat Algorithm, and 30% better than Improved Water Cycle Optimization. Computational times per iteration are also evaluated, demonstrating a significantly lower time of 151 µs compared to all other relevant methods discussed in this article.