The operation of photovoltaic (PV) panels in partial shade (PS) conditions (PSCs) lowers the quantity of energy generated due to the hot spots that increase the power loss. In this scenario, a maximum power point tracker (MPPT) is necessary to monitor the power–voltage curve, which features multiple local peaks in addition to one global peak (GP). The GP could not be extracted from the panel at PSCs using traditional MPPT techniques such as hill climbing (HC), perturb and observe (P&O), and incremental conductance (IC). Therefore, this paper proposes a new construction of MPPT simulated via a Raspberry Pi 4 controller programed through a recent bald eagle search (BES) algorithm. Because of its substantially enhanced diversification, which aids in avoiding local peaks, the suggested BES can capture the global power of PV during PSCs. The Raspberry Pi 4‐BES MPPT in the recommended hardware adjusts the direct current (DC)–DC boost converter’s duty cycle based on its inputs, which are the voltage and current from the PV panel output. Eight shade patterns are studied, and the fetched results through the proposed BES are compared to other programed approaches of skill optimization algorithm (SOA), gray wolf optimizer (GWO), and particle swarm optimization (PSO). Additionally, the proposed tracker’s Simulink model is created and evaluated in relation to the experimental hardware. Moreover, the power errors and efficiencies of all considered approaches are calculated in both simulation and experimental analyses. The results revealed that the least errors are 0.0018% and 0.296% obtained through the proposed Raspberry Pi 4‐BES MPPT in simulation and experimental, respectively. Also, in the simulation and experimental setting, the recommended tracker yielded the highest efficiencies of 99.99% and 99.92%, respectively. The suggested Raspberry Pi 4‐BES MPPT is praised for being a useful tracker for monitoring the GP of PV panels at various PSCs.