International Journal of Scientific Research and Management (IJSRM) ||Volume||12||Issue||10||Pages||1647-1657||2024|| Website: https://ijsrm.net ISSN (e): 2321-3418 DOI: 10.18535/ijsrm/v12i10.ec11 Gireesh Kambala, IJSRM Volume 12 Issue 10 October 2024 EC-2024-1647 Intelligent Fault Detection and Self-Healing Architectures in Distributed Software Systems for Mission-Critical Applications Gireesh Kambala MD, CMS Engineer, Lead, Teach for America, USA. Abstract- Self-healing and intelligent fault detection systems are very vital frameworks if we are to raise the dependability and resilience of distributed software systems in mission-critical applications. By use of contemporary technologies including predictive analytics, machine learning, and adaptive algorithms, these systems independently repair errors, actively evaluate system health, and find anomalies: Among the techniques these systems apply to keep low operational costs, continuous service delivery, and little downtime are redundancy, failover systems, and real-time diagnostics. Systems with self-healing capability offer scalability and fault tolerance in both dynamic and demanding environments as well as in optimal performance with various workloads. Using reference to its main features, advantages, and techniques, this book discusses intelligent defect management. The focus is on how these satisfy the dependability standards in domains such aviation, finance, and healthcare. This highlights the possibility to reorganise these systems to enhance operational resilience and efficiency, hence strengthening the dependability and autonomy of dispersed systems. Keywords: Fault detection, self-healing architectures, distributed systems, mission-critical applications I. Introduction The explosive spread of distributed software systems has produced notable developments in vital fields including smart cities, finance, healthcare, and aerospace engineering. Distribution architecture, multi-node interactions, and real-time decision-making requirements define modern technical infrastructure. Their complexity and importance make them prone to flaws like hardware failures, software bugs, network interruptions, and security lapses. Failures in mission-critical distributed systems could cause operational disturbances, financial losses, and maybe jeopardise human life. Consequently, because they provide resilience, adaptation, and autonomy in these situations, intelligent fault detection and self-healing systems have become extremely important in research. [1]. Machine learning (ML), artificial intelligence (AI), edge computing these systems allow little human intervention proactive detection, analysis, and fault mitigating action. Intelligent fault detection systems find possible faults before they become serious issues using predictive analytics, anomaly detection methods, and real-time monitoring. Among other approaches, deep learning and reinforcement learning have shown to be very useful in enhancing the accuracy and efficiency of defect detection systems thereby enabling systems to identify minor trends suggestive of anomalies. Conversely, self-healing systems on the other hand use autonomous recovery mechanisms including dynamic resource allocation, micro services reconfiguration, and real-time replication of critical components to assure continuation of operations. Including self-healing features into distributed systems lowers mean time to recovery (MTTR), lessens service interruptions, and improves general system dependability. The acceptance of distributed ledger technologies such as