Path optimization is a crucial segment in the field of reliable autonomous navigation of unmanned vehicles. Prior work is primarily focused on generating the optimal path by considering factors such as the small sum of turning angles, smoother trajectories, and low time and space complexity with the shortest path from start to destination. These conditions have been fulfilled in the past by proposing several techniques such as path smoothing filters, selection of the optimal number of nodes, informed RRT*, improved A*, the advanced potential field method, etc. The focus of the research is to develop a modified version of the conventional Bezier curve path optimization approach that produces a smoother trajectory. The modified techniques optimized the number of control points by adding a suitable number of control points near the sharp turns. The proposed research work is compared with various renowned techniques such as traditional ACO, improved ACO with traditional Bezier, and ACO with midpoint techniques. The proposed technique is implemented in various experimental setups, and it is experimentally obtained that the number of collisions is reduced to zero with a time lag reduced by 23.52%, and the path length is reduced by 9.8% as compared to various renowned techniques. The proposed research provides a proficient solution to the path optimization problem and is quite reliable in the fields of autonomous navigation, drone navigation, trajectory planning of manipulators, etc.