Abstract
It is now obvious that the blistering growth of data on modern computational platforms has revealed the constraints of the classical storage designs, especially the rigidity of strict leveling, the existence of large latency, and the everincreasing cost of operation. Against these inadequacies, the current paper offers an adaptive storage-optimization model that incorporates the concepts of Reinforcement Learning, Long Short-Term Memory modeling, and Generative AI to make predictive and autonomous data management possible. The LSTM element predicts temporal access patterns, and the RL agent is an agent that dynamically categorizes the workloads into hot, warm and cold levels, thus resulting in a context-aware allocation strategy. Generative AI complements this decisionmaking process by modeling possible shifts in workload and predicting anomalies and, therefore, introduces an intelligent forward-thinking layer. The optimized Storage Optimization Controller (SOC) coordinates heterogeneous media using an integrated abstraction layer, based upon the training and orchestration of both TensorFlow and PyTorch along with the training and orchestration of OpenAI gym. Experimental analysis of the framework shows that there have been substantial throughput and latency reduction and that the energy overhead has been decreased, thus making AI-integrated storage a robust and cost-efficient platform of future data ecosystems.