This paper presents an AI-Powered Visualization and Recommendation System designed to analyze largevolumesof mobile application reviews and transform unstructured textual feedback into structured, actionable insights. Withtherapidgrowth of mobile applications across various domains, user-generated reviews have become an important indicatorofapplication quality, usability, and performance. However, manual analysis of thousands of textual reviews is inefficient andoften fails to uncover deeper trends and recurring issues. The proposed system integrates Natural Language Processing(NLP), Machine Learning (ML), and interactive visualization techniques to automate the analysis process. Thesystemperforms data ingestion, preprocessing, sentiment classification, theme extraction, and severity-based recommendationgeneration within a unified modular architecture. By operating in a standalone and privacy-preserving environment, thesystem ensures data integrity while supporting developers and product managers in making structured, data-drivendecisionsfor application improvement. Keywords: App Review Analytics, Sentiment Classification, Theme Extraction, Natural Language Processing, Machine Learning,Visualization Dashboard, Recommendation Engine.