In the modern mobile application ecosystem, user reviews play a critical role in shaping applicationquality, userretention, and overall market success. However, these reviews are typically unstructured, voluminous, and spreadacrossmultiple platforms, making them difficult for developers to analyze and act upon.This project proposes “AI-PoweredVisualization & Recommendation of Multi-App Reviews”, an offline system that leverages Natural Language Processing(NLP) and Machine Learning (ML) techniques to transform raw user reviews into meaningful insights. Reviews areingestedthrough local CSV/Excel files and stored in a lightweight SQLite database, ensuring complete independence fromthird-partyAPIs or external services. The system applies preprocessing steps such as tokenization, stopword removal, and lemmatization,followed by a sentiment classification pipeline using TF-IDF and supervised learning models (Logistic Regression, SVM).The processed results are presented through an interactive GUI-based dashboard that features pie charts, bar graphs, andtemporal trend visualizations, enabling developers to track user sentiments over time and across versions. Inaddition, thesystem incorporates a recommendation engine that identifies recurring issues (e.g., crashes, UI problems, battery drain) andtranslates them into prioritized, actionable suggestions for developers. Export functionality further supports local reportinginCSV/PDF-formats. By unifying offline sentiment analysis, visualization, and recommendation, the proposed system empowers developerswithdata-driven decision-making while ensuring privacy, reliability, and ease of deployment in academic and industrial contexts.Keywords Sentiment Analysis, Opinion Mining, Natural Language Processing, Machine Learning, SQLite, GUI, DataVisualization, App Reviews, Recommendation Engine, Offline Analytics