Experimental Evaluation of AI-Driven Sustainable Intelligent Cloud Optimization Framework

This paper presents an integrated multi-modal Artificial Intelligence (AI) framework that uses intelligent file prioritization, redundant storage reduction, and automated content understanding to improve personal cloud environments. The system makes use of AI-driven categorization, vision-language processing for image title suggestion, and large language models (LLMs) for hierarchical document and video summarization. The suggested framework integrates multiple AI services into a single architecture, in contrast to traditional storage platforms like Dropbox and Google Drive. Effective file prioritization, high summarization compression efficiency, and enhanced retrieval accuracy are demonstrated by experimental evaluation across several modules. The outcomes confirm that creating an intelligent, context-aware digital workspace from traditional cloud storage is feasible.