This research presents the Temporal-Contextual Micro-Moment Prediction Framework (TCMP), an innovative approach for forecasting Fast-Moving Consumer Goods purchas- ing behavior that mitigates significant deficiencies in current pre- dictive approaches. The system uses advanced machine learning algorithms to combine data from several sources, such as social media sentiment, real-time behavioral analytics, and traditional transaction data, to forecast purchase intent over several time periods. The study addresses three fundamental gaps in current FMCG predictive analytics: temporal granularity limitations that focus on either immediate or long-term predictions without capturing intermediate decision phases; micro-moment context integration challenges that treat consumer interactions as isolated events rather than connected sequences; and real-time feature engineering constraints that create prediction lag in dynamic market environments. The study makes important theoretical progress in predicting consumer behavior over multiple time periods and gives useful frameworks for putting the ideas into practice in the actual world. Some of the most important new ideas are streaming feature computation techniques, cross- platform social media integration methodologies, and privacy- preserving analytics approaches that keep predictive efficacy while following the rules. These contributions directly address the pressing commercial requirement for precise and agile consumer behavior forecasting in progressively dynamic and competitive fast-moving consumer goods (FMCG) sectors.

Terms—Micro-Moment Intent Prediction , Machine learning, Fast-Moving Consumer Goods (FMCG), Predictive Analytics, Demand Forecasting, Consumer Behavior Analytics, Scalable Data Analysis, Dynamic Market Environments

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