The blockchain ecosystem operates in rapidly changing perspectives based on market volatility, investor sentiments, regulatory ambiguity, and transaction fees, so risk management and economic optimization becomes inevitable. This fuzzy logic framework implementation is based on a Mamdani Fuzzy Inference System (FIS) in MATLAB that integrates risk evaluation with economic decision optimization for Blockchain-based economies. Our proposed FIS model uses fuzzy membership functions and a rule-based inference system to process these four important input parameters and provide output indicating risk assessment. Factors such as market volatility, investor sentiment, regulatory uncertainty, and transaction fees create challenges and opportunities for market participants, shaping the behavior of buyers and sellers and influencing investment strategies. Through fuzzy framework, it gives a strong way to deal with the uncertainties occurring in the blockchain operations and also to take best decisions. FIS toolbox in MATLAB will enhance the interpretability of results. Contour and surface graphs show how risks vary along different input conditions while the scatter diagram reveals trend in economic behavior. A coloured gradient of the risk scores, both visually and dynamically, is shown by the heatmap, and some insight into the fuzzy sets describing input–output relationships is provided by the membership function plots. These visualizations will enable financial decision makers to effectively interpret the patterns of risks and adjust economic strategies appropriately. These results validate the model’s ability to spot high-risk conditions, optimize transaction strategies, and improve economic stability within blockchain networks. Also, this adaptive data-driven methodology provides investors, policymakers, and developers with potent sources of risk mitigation and strategic decision-making in decentralized financial ecosystems.