<p>The operational durability of Wireless Sensor Networks (WSNs) used in environmental monitoring, healthcare and industrial automation decreases due to sensor node failures, energy depletion, and communication breakdowns. The fault tolerance and the optimization of energy used in the operations are necessitated by the issues that develop as a result of these problems. The existing fault-tolerant methods do not provide an efficient fault recovery with low latency and high network performance. The study presents the concept of BIAFTEL (Bio-Inspired AI-Driven Frameworks of Fault Tolerance and Extended Lifespan) that unites bio-inspired learning algorithms with the predictive fault tracking of AI-based methods to augment the fault tolerance and long-life time of WSNs. The biological resilience model affects BIAFTEL because of its applications of self-healing techniques and swarm intelligence combined with adaptive behaviours, which allow automated fault-detection, routing optimization, and network outage. The main innovation of the system identifies faults through LSTM Autoencoders based fault detection techniques which train normal operating patterns to spot anomalies before they happen hence enhancing prediction precursors. HABCO serves as the routing mechanism because it enables adaptive energy-efficient networking that allows for lower power usage and better network lifetime extension. The network operation is executed through Python programming. Experimental results show that BIAFTEL achieves a 99.3% fault detection accuracy, reduces recovery time to 8.0 ms, and enhances energy efficiency by 20–25% compared to traditional approaches. These advancements make BIAFTEL particularly suitable for mission-critical WSN applications in smart cities, healthcare, and environmental monitoring, where reliable, long-lasting sensor networks are essential.</p>