AI-Driven Predictive Optimization for Data Center Efficiency: a Web-Based Calculator with Machine Learning Integration

New optimization solutions are needed for existing current data center energy utilization levels. The predictive AI optimized technology is used within the designed framework as it operates within the framework of a web based calculator system to analyze data center performance efficiency. A machine learning methodology for power usage prediction and thermal signatures recognition is done by a computer system with various machine learning methods including regression models, deep neural networks and reinforcement learning for finding the optimal workload allocation and unsupervised learning to detect anomalies of the system. This method was demonstrated three performance evaluations across various data center scenarios, saving 25–40% of energy while attaining a PUE 15– 30%, and reducing the operation cost 30–45%. This adaptation takes 2-5 minutes and performs better than 87 to 93% compared with standard strategies. This online interface allows an expert of any background to use sophisticated optimization tools through its conversion of complex metric information into visual presentations. Research showed that the cost benefit of implementing this approach lie in the range of 9 to 14 months per research findings depending on the setup of data center, making this approach economically viable across the board. Through its novel method of obtaining sustainable data centers with computational capacity retention, the research considerably decreases major deficiencies in existing solutions.