Tax optimization continues to be a complicated issue as financial habits are individual responses to regional tax policy changes. This research presented an innovative tax optimization framework powered by AI, including multiple-output neural networks, clustering algorithms and NLP, to provide personalized, hyper-localized tax savings strategies. The proposed framework accomplishes this by predicting salient financial metrics, such as tax non-liability, adjusted gross income, and even refunds. The clustering algorithms predict user personas and group taxpayers in the same ZIP code in order to develop clustering profiles within a region, discovering local patterns in different tax deductible expenses. The NLP (Natural language processing) engine self-identifies unapplied tax credits, thus possibly increasing almost 35% of potential refunds. Because of real-time adaptivity, the framework can respond as financial inputs change, particularly with last-minute or end-of-year contributions and deductions, which maximizes most deduction valuations and enhancements to accuracy will naturally reduce audit risk. The experimental results suggest prediction accuracy of over 93%, in addition to greater refund optimizations, greater audit reductions, and more strategic financial layouts. The methodology proposed in the patent provides patent level innovations in clustering based tax strategy, dynamic optimization, and autonomous discovery of credits. As such, the foundation for next-generation AI tax planning systems has been established for individual and small business use.