The operation of photovoltaic (PV) module under partial shadow conditions considers a big challenge for most researchers due to power loss and hot spots that reduce the amount of extracted power. In such an operation, the panel voltage–power curve has a unique global maximum power (GMP) to be tracked. Therefore, this paper proposes a new maximum power point tracker (MPPT) implemented by Raspberry Pi 4-based embedded board programmed via two metaheuristic approaches of cuckoo search (CS) and particle swarm optimizer (PSO). The approaches are developed using python software programming language to adapt the duty cycle fed to the MOSFET of DC/DC boost converter connected to the panel terminals. The panel is simulated in Simulink/Matlab library to identify the GMP in each studied case. An experimental setup is conducted in the lab room of the college of Engineering, Jouf University, Saudi Arabia to assess the proposed tracker. Moreover, eight shade patterns are considered via covering 10% to 80% with step 10% of panel with shadow. Furthermore, statistical tests of the Wilcoxson sign rank test and ANOVA are conducted to assess the validity of the proposed tracker. The obtained results are compared to perturb and observe (P&O) and gray wolf optimizer (GWO). The PSObased tracker achieved the best efficiency of 96.92%, the CS achieved 93.62%, and GWO get an efficiency of 93.15%. Additionally, on the side of Wilcoxson sign rank and ANOVA tests, the PSO outperformed CS and GWO. The results confirmed the superiority of the proposed Raspberry Pi system programmed via PSO over that of CS and GWO in enhancing the power generated from the panel operated at different partial shades