Environmental, Social and Governance (ESG) scoring plays a critical role in sustainable investing and risk analysis. However, current ESG analytics target institutional investors, leaving everyday consumers unaware of the ethical footprint of their own spending. This paper introduces an AI-powered ESG Spending Analyzer that allows users to upload personal transaction files, automatically map merchants to corporations using Natural Language Processing (NLP) and fuzzy matching, and compute explainable ESG scores derived from a curated master database built via public ESG sources (https://yfinance, Sustainalytics). The system integrates ingestion, entity recognition, price inference, ESG aggregation, and alternative recommendations into a Streamlit dashboard. Results show reliable merchant mapping (85% accuracy) and 72% faster reruns through caching. This approach represents a novel extension of ESG analytics from corporate to consumer scale, democratizing sustainability awareness through interpretable AI.