Sentiment analysis has become one of the most important research areas in Natural Language Processing(NLP) duetothe rapid growth of multilingual user-generated content across social media platforms, online reviews, blogs, discussionforums,and e-commerce websites. The increasing availability of opinion-rich data in multiple languages has created significant challengesin accurately identifying user sentiments because of linguistic diversity, vocabulary variations, grammatical differences, andlimited language-specific resources. Machine learning techniques have emerged as effective solutions for multilingual sentimentclassification by automatically learning discriminative features from textual data without relying solely on handcraftedlinguisticrules. This experimental study investigates the comparative performance of widely used machine learning algorithmsformultilingual sentiment analysis by evaluating their classification accuracy, precision, recall, F1-score, computational efficiency,and language adaptability. The proposed framework integrates multilingual text preprocessing, feature extraction, machinelearning classification, and performance evaluation into a unified analytical architecture. A mathematical frameworkandalgorithmic strategy are developed to measure model effectiveness, feature relevance, classification consistency, andmultilingualprediction performance. Experimental evaluation demonstrates that supervised machine learning models significantlyimprovemultilingual sentiment classification while exhibiting varying levels of performance across different languages andfeaturerepresentations. The proposed framework provides valuable guidance for researchers, language technology developers, anddatascientists seeking to design accurate, scalable, and efficient multilingual sentiment analysis systems. Keywords: Machine Learning, Multilingual Sentiment Analysis, Natural Language Processing, OpinionMining, TextClassification.