In fuzzy heuristic search algorithms embedded with reinforcement learning have been proven to be a highly capable technique for the degree of tuning as well. The performance of traditional heuristic search algorithms and single-reinforcement learning models is limited by inefficiency in dynamic environments. We propose a new combined fuzzy logic and reinforcement learning model for decision enhancement and system optimization. Online parameter adaptation through fuzzy heuristic search combined with reinforcement learning enhances scalability in both variations of the algorithm. Fuzzy logic uses linguistic variables and membership functions to account for uncertainty or imprecision, while reinforcement learning learns optimal policies through interaction with the environment. Following the above objective, the model tries to increase the convergence rate by 25%, reduce computational time by 30%, improve solution reliability (20%), and finally adapt better (+ 40% compared with traditional methods). Resulting contributions include substantial improvements in search efficiency and quality of solutions, reductions in computational effort, and an improved generalization capacity of intelligent systems—thus pushing the boundaries forward on optimization methodology.