The evaluation of Purchase Power Parity (PPP) through traditional methods mainly depends on macroeconomic data including price indices together with GDP statistics and exchange rates. The research develops an AI-enhanced analytical system that conducts experimental Social Media Utility-based Purchase Power Parity (PPP) evaluations through financial consumer insights from FinTech information platforms. The computational framework implements Natural Language Processing technology on unfiltered social media information and FinTech behavioral patterns to develop an index measuring digital spending behavior influence known as Social Media Utility Index (SMUI). Deep learning LSTM and CNN models along with reinforcement learning-based adaptive adjustment processes personal finance data to match it with global PPP metrics obtained from World Bank and IMF institutions. The AI platform performed sentiment analysis by reaching an average accuracy level of 91.4% for expense predictions and produced 88.7% accuracy when analyzing PPP-adjusted behavioral activities. Research conducted in 10 worldwide regions demonstrates that online utility ratings establish solid statistical relationships with the spending power observed in each area. The proposed framework introduces a fresh perspective that both interprets PPP as both a quantitative economic tool and as a people-centered market construct. The system provides XAI features that establish transparency as well as practical interpretability options for users. The research integrates economic concepts with AI analytics to create customized financial systems which enable worldwide economic flexibility