This paper proposes an advanced intelligent application of Enhanced Artificial Gorilla Troops (EAGT) optimizer for parameters extraction of three different PV modules. The proposed EAGT optimizer is inspired by gorilla group behaviors, in which different methods are replicated, including migration to a new location, migrating to other gorillas, migration toward a designated spot, following the silverback, and competing for adult females. The EAGT is improved by supporting the exploration phase involving a fitness-based crossover (FBC) strategy. Not only that, but also it is by supporting the exploitation phase involving a periodic Tangent Flight (TF) operator. The effectiveness of the proposed EAGT is demonstrated using numerical assessments for the Kyocera KC200GT and STM6-40/36 PV modules using the Triple-Diode Model (TDM). In addition, the proposed EAGT is compared to the results of contemporary algorithms such as jellyfish search optimizer, forensic-based investigation optimizer, heap optimizer, equilibrium optimizer, and marine predator's optimizer. Also, the proposed EAGT is effectively applied on the SP70 PV module subjected to varied levels of sun irradiances and temperatures. The EAGT optimizer's efficacy and superiority are signified by fitness function standard deviations that indicate that TDM are less than 1 × 10−7, and compared to current and reported findings by others.