aspg-21-2611-1791465341 20261008131541 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2024 23 4 Adam Mohamed Omer Accounting Program, Applied College at Muhyle, King Khalid University, Kingdom of Saudi Arabia Fadoua Kouki Applied Management Program, Applied College at Muhyle, King Khalid University, Kingdom of Saudi Arabia Adeeb Alhebri Accounting Program, Applied College at Muhyle, King Khalid University, Kingdom of Saudi Arabia Radwan Hussien Alkebssi Assistant Professor of Accounting, Business School, Xi’an International studies university, China Ebrahim Mohammed Al-Matari Accounting Department, College of Business, jouf University, Kingdom of Saudi Arabia Predicting sustainable financial crises and promoting green finance are paramount in fast developing economic landscape. Leveraging advanced AI-driven technologies, such as Neutrosophic logic, enables a nuanced understanding of complex sustainability factors influencing financial markets. By incorporating these advanced technologies, organizations can proactively mitigate and identify risks related to unsustainable practices while fostering investment aligned with environmental, social, and governance (ESG) principles. This proactive stance improves financial resilience and contributes to the transition towards a resilient and more sustainable financial ecosystem. We can navigate future challenges with foresight and responsibility through the synergy of sustainable financial crisis prediction and green finance initiatives, which ensures a prosperous and environmentally conscious financial future for the generation to come. This study develops a new optimal Fuzzy Parameterized Single-Valued Neutrosophic Subset for financial crisis prediction and green finance (OFPSVNS-FCPGF) technique. The OFPSVNS-FCPGF technique intends to recognize the presence of the financial disaster in the sustainable and green finance sector. In the OFPSVNS-FCPGF technique, Z-score normalization is primarily used to measure the economic data into a beneficial layout. For the procedure of prediction, the OFPSVNS-FCPGF approach designs the FPSVNS approach which detects the occurrence of financial crises or not. Furthermore, the parameter tuning of the FPSVNS technique takes place utilizing the grasshopper optimization algorithm (GOA). To illustrate the improved FCP outcomes of the OFPSVNS-FCPGF model, a series of simulations were involved. An wide comparison study specified that the OFPSVNS-FCPGF method gains significant outcomes in the green finance sector. 2024 308 322 2611 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.230424 https://www.americaspg.com/journal/21/article/2611 https://www.americaspg.com/storage/71711205163.pdf