An efficient and reliable operation of a mobile robot is crucial in several applications, such as transportation, search, surveillance, and rescue. This paper proposes an intelligent approach to analyze the attitude of the mobile robot and thereby predicting the faults in them in an early stage. Prior to become faulty, a mobile robot behaves unusually. These unusual behaviors such as random walk, repeat computation, producing same next position, or reporting incorrect position. These patterns are recorded and analyzed using a time delay neural network (TDNN) for analysis and fault prediction. The TDNN-based algorithm identifies the precursors to robot faults by learning the relationship between the unusual behavioral patterns and the impending fault. Experimental results demonstrate the effectiveness of the proposed approach in accurately predicting robot faults, allowing for proactive maintenance and mitigating disruptions to mission-critical operations. The findings of this study contribute to enhancing the reliability and autonomy of mobile robotic systems.