Abstract

This paper will explore the creation and execution of self-healing cloud architecture, which has been crafted with autonomous retail settings in mind. With the retail industry moving towards cashier-less systems, the need for 100 percent system uptime and secure transaction processing has become critical. This study proposes a framework that leverages artificial intelligence and machine learning to identify anomalies and fraudulent patterns within the system in real-time and enables the cloud infrastructure to automatically fix errors without human intervention. The synthetic dataset is used in the study with 185 transaction instances, which include latency, packet loss, and security scoring. The implementation leverages Python-based environments and machine learning libraries of predictive maintenance and fraud classification. The findings indicate that the self-healing processes considerably decrease down time and significantly enhance the detection of advanced fraud attempts. The proposed system will make autonomous retail platforms resistant to technical breakdowns and external attacks by incorporating predictive analytics into the cloud fabric itself. The paper will offer a detailed description of the performance indicators, and it will be a roadmap for future-proof retail technology, which will focus on reliability and security.

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