Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3761 2019 2019 University of Sharjah, UAE; Tashkent State University of Economics, Uzbekistan N. N. International Islamic Academy of Uzbekistan, Department of Islamic Economics and Finance, Pilgrimage Tourism, Uzbekistan Olim Astanakulov Tashkent State Pedagogical University named after Nizami, Uzbekistan Umarova Navbakhor Shokirovna One of the most effective devices to model uncertainty in decision-making difficulties is the neutrosophic set (NS) and its extensions, namely interval NS (INS), interval complex NS (ICNS), and complex NS (CNS). An effective device to demonstrate ambiguities and uncertainty in decision-making is the NS, which is the more conventional standard set, intuitionistic fuzzy set (IFS), and fuzzy set (FS) by including 3 scores of falsehood, indeterminacy, and truth of established statements. Financial risk management is a massive field with different and developing modules, as demonstrated by either its historic growth or present classic example. It is a procedure to address the uncertainty originating from financial markets. It consists of calculating the financial threats dealing with organization and emerging management tactics by internal policies and priorities. A risk-management method is an experience control and accounting system. In this manuscript, we develop an Intelligent Risk Management Approach for Financial Crisis Using Pythagorean Neutrosophic Fuzzy Graphs and Metaheuristic Optimization Algorithms (IRMFC-PNFGMOA). The main intention of IRMFC-PNFGMOA technique is to analyse and develop effective methodologies for measuring and managing financial risk in dynamic market conditions. Initially, the data pre-processing stage applies Z-score normalization to clean, transform, and structure raw data to improve the quality. Besides, the Aquila optimization algorithm (AOA) has been deployed for the selection of feature processes to identify and retain the most relevant features from input data. For the classification process, the proposed IRMFC-PNFGMOA model designs pythagorean neutrosophic fuzzy graphs (PNFG) technique. To further optimize model performance, the growth optimizer (GO) algorithm is utilized for hyperparameter tuning to ensure that the best hyperparameters are selected for enhanced accuracy. To exhibit the enhanced performance of the presented IRMFC-PNFGMOA model, a comprehensive experimental analysis is made. The comparative results reported the improvised characteristics of the IRMFC-PNFGMOA model. 2025 2025 345 355 10.54216/JISIoT.160224 https://www.americaspg.com/articleinfo/18/show/3761