The use of MPPT control technology has proven to be the most effective way to improve the performance of photovoltaic systems (PVS), especially in partial shading conditions (PS-C). Therefore, accurately determining the “maximum power point (MPP)” is crucial in PVS design. Thus, many MPPT methods have been proposed in the literature, ranging from simple to complex algorithms. However, choosing the best strategy for PVS implementation can be challenging due to the wide range of choices. Recently, metaheuristic optimization algorithms, machine-learning models, and their hybridization have attracted much attention from researchers due to their multiple advantages in solving optimization challenges. In view of these advantages, this paper focuses on the examination of the recent advancements in hybrid intelligent MPPT techniques operating under PS-C published over the last five years. The paper evaluates and compares all selected and classified categories based on diverse criteria such as computational complexity level, percentage of efficiency, convergence speed, converter, application and configuration type, and validation, discussing the strengths and weaknesses of each approach and suggesting directions for future research. Results from evaluation and comparison highlight the promise of hybrid intelligent solutions in improving MPPT technique performance.