Increasing reliance on GPS-based services in smart cities introduces new vulnerabilities. This is especially vulnerable to GPS spoofing attacks. These attacks distort GPS signals and confuse customers. This seriously threatens the accuracy and reliability of urban systems such as autonomous vehicles. Traffic management and analytical GPS navigation services-A comprehensive framework is offered to detect spoofing attacks. The framework combines rule-based detection with advanced algorithms. Includes Kalman filter for predictive tracking. and dynamic time warping (DTW) for trajectory similarity analysis. Using a dataset of 2,000 GPS points to simulate vehicle movement and data, A data spoofing attack is triggered when a subgroup is triggered. Change. To demonstrate its effectiveness in detecting simple and complex spoofing situations. The framework's performance is evaluated using standard metrics such as precision, recall, F1 score, etc. The proposed solution provides a scalable, real-time approach to enhance the security of GPS service systems in conditions surrounding the smart city.