Fire rescue operation scenarios are a hazardous environment that requires quick responses as well as accurate judgment. This paper presents the automata-driven fire rescue bot, an intelligent system that leverages non-deterministic finite automata (NFA) and Turing machines (TM) to optimize pathfinding and decision-making. The JFLAP tool has been used to simulate NFA and TM mechanisms. Pygame has been used to implement a working model of the environment and movements of the bot by using the condition-based mechanism of NFA. The study demonstrates how foundational concepts from the theory of computation can be applied to design a practical, scalable, and intelligent fire rescue solution.