In this paper, projectile trajectory optimization is investigated. The main objective is to determine the optimal launch angle that maximizes the projectile achieved total distance (arc length). We investigated the process of determining the most effective launch angle, the angle at which an object is projected to achieve the greatest horizontal distance covered. One of the key determinants of this angle is the initial velocity of the projectile, the impact of air resistance, and the nature of the landing surface. The application of the reinforcement Q-learning to optimize the projectile total traveled distance is explored whereas, traditional methods of optimizing projectile trajectories often rely on mathematical models and iterative approaches. However, in this study, we leverage the flexibility and adaptability of Q-learning to optimize projectile trajectory by learning optimal actions through interaction with the environment. The result is proven graphically, furthermore, the performance of the achieved range and the maximum height at the optimum angle is investigated.