The study of sophisticated route-planning techniques is a result of the increasing demand for quick drone delivery services. This study introduces a novel method that blends neural network predictions and Ant Colony Optimization (ACO). ACO draws inspiration from the way ants use pheromones or smell trails to choose the optimal routes. This aids in determining effective distribution routes, as does the use of neural networks—computer models that learn from data. The affordability of the drone utilized in this study is an intriguing feature. With an Arduino Nano microprocessor and an MPU6050 gyroscope and accelerometer, the drone can fly steadily and affordably. Data from Google Maps is included into these routes to make them more useful. Roads, buildings, and shifting traffic patterns are examples of real-world difficulties that are considered. This method's eco-friendly approach is yet another important benefit. The drone minimizes its environmental effect while covering a greater area with less fuel thanks to its fuel-optimization system. This blended model's preliminary testing yields encouraging findings. This method provides higher fuel savings and faster delivery times than conventional routing approaches. This study offers a thorough analysis of this combination approach, emphasizing how it may become the new norm for reasonably priced drone delivery.