International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/2654 2020 2020 Applied College, Khamis Mushait, King Khalid University, Abha, Saudi Arabia Abdelgalal Abdelgalal Resolving financial futures through inverse problem-solving delves into the complicated process of deciphering the difficulties subjective in the financial market to forecast behaviours and future trends. Inverse problem-solving involves working backwards from observed outcomes to uncover the underlying conditions or parameters, unlike prediction models, which often rely on past information to predict future outcomes. This method in the finance sector includes untangling the numberless factors influencing the market dynamics, like technological advancements, economic indicators, investor sentiment, and geopolitical events. Analysts can tease out hidden patterns and relationships within financial data using statistical techniques and complex mathematical algorithms, enabling them to generate accurate predictions of market volatility, asset prices, and other crucial metrics. The financial future becomes less opaque through the lens of inverse problem solving, providing policymakers and investors great foresight and insight into navigating the uncertainties of global markets. Hence, this study introduces a Neutrosophic MOOSRA with Whale Optimization Algorithm (NMOOSRA-WOA) for Unraveling Financial Futures through Inverse Problem Solving. The NMOOSRA-WOA incorporates linear scaling normalization, NMOOSRA-based prediction, and WOA-based parameter tuning to boost the robustness and accuracy of financial predictions. The NMOOSRA technique generates predictions based on past financial time series data. Moreover, the framework integrates the Whale Optimization Algorithm (WOA) for parameter tuning, leveraging whale pods' search abilities to optimize predictive performance and finetune model parameters. The NMOOSRA-WOA provides a comprehensive algorithm for financial prediction by synergistically combining these methodologies, which facilitates more accurate forecasts of market trends, asset prices, and other critical indicators. Experimental results on real-time financial datasets demonstrate the superiority and efficacy of the proposed framework over other classical prediction techniques, highlighting its potential for risk management within dynamic financial markets and real-time applications in investment decision-making. 2024 2024 136 147 10.54216/IJNS.240113 https://www.americaspg.com/articleinfo/21/show/2654