The strategic expansion of business operations is a critical yet high-risk endeavor, often hampered by a reliance on macro-level market data that fails to capture localized nuances. This paper addresses this challenge by introducing a comprehensive framework for micro-market analytics, designed to facilitate data-driven, precision-targeted business expansion. The proposed model integrates multi-source data— including demographic, geospatial, transactional, and psychographic information—to segment large urban areas into distinct micro-markets. By applying machine learning algorithms, specifically a gradient boosting model, the framework generates a \"Market Potential Score\" to quantify the success probability for each granular location. The methodology is validated through a hypothetical case study of retail expansion in a Tier-2 Indian city, demonstrating its ability to identify high-potential, low-risk opportunities that traditional analysis would overlook. The framework culminates in a visualization dashboard, providing stakeholders with an intuitive tool for strategic decision-making. This approach significantly enhances the precision of expansion strategies, minimizes financial risk, and promotes sustainable business growth in competitive environments.