Title: Innovating for Environmental Sustainability through AI-Enabled Predictive Data Centre Maintenance


Authors: Gokul A, Booma Jayapalan, Giriraja Thuruvan G


Published in: Volume 3 Issue 1 Jan June 2026, Page No. 116-122


DOI: 10.63844/IJAITR.v3.i1.2026.116-122 cite


Keywords: Edge computing, sensor networks, data centre infrastructure management, AI predictive maintenance, hardware fault detection, self-healing systems, predictive analytics, energy efficiency, and environmental sustainability..


Abstract: Management of data centres is changing based on the adoption of artificial intelligence-based predictive hardware fault detection and maintenance methodologies. Reactive and schedule-based traditional methodologies of maintenance are significantly ineffective for servicing modern data centre infrastructures, resulting in rising operating costs, downtime, and workplace hazards. This study determines how innovations in Machine Learning and Artificial Intelligence are redrafting data centre operations by facilitating accurate failure prediction, real-time optimization, and self-managing infrastructure. Advanced sensory systems continuously measure hardware parameters such as temperature, voltage, vibration, and electrical consumption of hardware and feed data for analytics streams. AI models including neural networks, decision trees, and support vector machines act on this data to predict potential hardware failures and enable proactive self-healing activities such as performance optimization, component replacement, and software patching before failure impacts service availability. The research adopts a descriptive-analytical method based on case studies of industry and research conducted between 2020 and 2025. Results indicate that AI-based predictive maintenance decreases system failures by 30-50%, increases energy efficiency by a maximum of 40% by intelligent chilling, and optimizes real-time resource allocation. Besides enhancing work performance, it also enhances green sustainability by minimizing energy consumption, increasing hardware durability, and electronic waste reduction. Strategic execution centres on integration of systems, information management, and employee alignment. The research concludes by saying that an integration of human expertise and AI prowess leads to more resilient, energy-efficient, and green data centres, driving the mission of "Innovating for Environmental Sustainability."


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