Wireless robotic systems are increasingly deployed in complex and dynamic environments where uninterrupted operation is critical. Ensuring real-time fault detection is essential to maintain the safety, reliability, and autonomy of these systems. Traditional rule-based and statistical fault detection methods often fall short in capturing the intricate, non-linear behaviors inherent in modern robotic operations, particularly under wireless and resource-constrained conditions. This study investigates the application of deep learning for real-time fault detection in wireless robotic systems, aiming to develop a robust framework capable of identifying faults across sensors, actuators, and communication channels. The research integrates simulation tools such as ROS, Gazebo, and NS-3 to generate realistic robotic telemetry under controlled fault scenarios. Public datasets including the NASA C-MAPSS and bearing datasets are utilized alongside sensor logs from simulated robotic environments. Deep learning models—specifically Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Autoencoders—are implemented and evaluated for fault classification and anomaly detection. Experimental results demonstrate that these models achieve high detection accuracy and low inference latency, outperforming traditional methods and proving suitable for real-time edge deployment. The findings underscore the potential of deep learning to enhance the resilience and intelligence of future autonomous robotic platforms, laying the groundwork for self-monitoring, adaptive, and fault-tolerant robotic systems.