Designing an AI-Predictive Maintenance Model for E-Commerce Systems Using Machine Learning and Cloud Analytics
Winner Mayo, Jolly I Ogbole, Precious Osobhalenewie Okoruwa, Odunayo Mercy Babatope
Abstract
The rapid expansion of e-commerce ecosystems has intensified the need for resilient, high-performing, and continuously available digital infrastructures. Predictive maintenance powered by Artificial Intelligence (AI) and Machine Learning (ML) offers a transformative approach to ensuring operational continuity by proactively identifying, diagnosing, and mitigating system failures before they occur. This review explores the design and implementation of AI-driven predictive maintenance models tailored for e-commerce systems, integrating cloud analytics to enable real-time monitoring and decision-making. The study examines key machine learning techniques—such as anomaly detection, time-series forecasting, and deep reinforcement learning—used to predict transactional bottlenecks, network downtimes, and server anomalies. It further discusses the architecture of cloud-based predictive maintenance platforms leveraging scalable data lakes, IoT telemetry, and edge analytics for fault detection and system optimization. Emphasis is placed on model explainability, data governance, and cybersecurity integration to enhance trust and regulatory compliance. By synthesizing recent advancements in AI-ML frameworks and cloud infrastructures, the paper identifies best practices, challenges, and future research directions for developing intelligent, adaptive, and cost-effective maintenance systems in e-commerce environments. Ultimately, the research underscores the strategic value of AI-driven predictive maintenance in sustaining consumer trust, optimizing digital operations, and achieving business resilience in competitive online markets.