A large number of researchers recently focused on intelligent analysis about algorithms for path-finding concerning games. Currently, the most modern application in game map path-finding is based on the fuzzy logic (FL) algorithm. It demonstrates that these strategies increase the quest performance and have a simpler direction to enhance the algorithm’s game. Hence, in this paper, a Fuzzy logic-based game path-finding Framework (FLGPFF) has been proposed to reduce the search space to improve search speed and path planning on uneven surfaces. The suggested FLGPFF method uses computer vision to simulate the fuzzy dynamic game path’s algorithm using possible multi-purpose platforms. A computer vision explores the whole game map path and finds a way between two remote places. The fuzzy method uses a fuzzier, center average demulsifier, and product inference engine. The ant colony algorithm is used for the complex environment with many moving obstacles and, using a weighted artificial field method, it calculates a trajectory from the target’s initial location. Thus, the experimental results show the FLGPFF to enhance path allocation prediction and less delay time than other popular methods. The simulation outcome recommended that FLGPFF can improve the accuracy ratio (95.2%), search timer per map (96.3%), game quality improvement ratio (98.5%), performance ratio (97.8%), and comparison of the path-finding model (95.1%).