The increasing digitization of business operations has led to an unprecedented explosion of big data, presenting both challenges and opportunities for organizations striving to enhance customer engagement, loyalty, and market positioning. Machine learning (ML) and sentiment analysis, integrated with big data analytics, have emerged as powerful tools for understanding consumer behavior, predicting market trends, and personalizing customer interactions. By analyzing structured and unstructured data from various sources—such as social media, customer reviews, and transaction records—companies can gain real-time insights into consumer sentiment, preferences, and pain points, allowing them to tailor marketing strategies and improve customer experience. Machine learning algorithms play a crucial role in segmenting customers, forecasting purchasing patterns, and optimizing retention strategies through predictive modeling. Sentiment analysis, leveraging Natural Language Processing (NLP), enables businesses to assess consumer emotions and brand perception, facilitating proactive engagement and reputation management. Companies employing these techniques have seen increased customer lifetime value, improved brand affinity, and enhanced competitive positioning. However, despite these advantages, challenges such as data privacy regulations, algorithmic biases, and integration complexities must be addressed to ensure ethical and effective deployment. This paper explores the methodologies and applications of big data-driven sentiment analysis and machine learning in customer-centric decision-making. By strategically harnessing these technologies, organizations can achieve sustainable growth, customer satisfaction, and a stronger foothold in an increasingly competitive digital economy.