As cities grow smarter and more connected, so do the threats lurking in our transportation networks. Intelligent Transportation Systems (ITS), while revolutionary, are vulnerable to unexpected disruptions, ranging from cyber intrusions to sensor malfunctions and abnormal traffic behavior. This study explores how autoencoders, a class of unsupervised neural networks, can serve as a powerful tool for anomaly detection in ITS environments. By learning the “normal” patterns in traffic data, these models flag deviations that may indicate malicious activities or system faults, without relying on labeled datasets. We propose and evaluate a tailored autoencoder framework trained on real-time vehicular and sensor data from urban ITS infrastructure. Experimental results show high sensitivity and low false positive rates in detecting various types of anomalies, even in noisy and incomplete data scenarios. This approach not only enhances early warning capabilities but also minimizes operational disruptions. The research underscores the promise of autoencoders in fortifying the safety and resilience of next-generation transportation systems.