Global economic resilience relies on our ability to predict and mitigate financial crises, especially for small and medium-sized enterprises (SMEs)—vital drivers of economic growth. These SMEs are particularly susceptible to market fluctuations in business-to-business or consumer-focused sectors. Organizations’ integration of big data technologies has revolutionized global financial data management, enhancing our resilience. In our interconnected world, the timely identification of impending financial crises is crucial. It's the linchpin to prevent catastrophic collapses that could send shockwaves through the global economy and societies. To address this challenge, we introduce the Nature-inspired Red-optimized Stochastic Artificial Neural Network (NRFO-SANN), a powerful instrument for detecting global financial crises and anomalies. Our approach leverages a diverse array of financial data collected worldwide. Employing Min–max normalization, we meticulously pre-process the data, ensuring its readiness for analysis. Principal Component Analysis (PCA) extracts the core features crucial for crisis identification. These insights fuel the implementation of the NRFO-SANN method, unlocking the potential of AI-driven prediction. The results are remarkable. Our NRFO-SANN model not only outperforms its peers but does so resoundingly. With an impressive 96% accuracy rate, it operates efficiently, taking just 1 s for computations. It boasts an F-score of 96.5%, a sensitivity of 94% and a specificity of 95%. This model equips us with a robust tool for anticipating and responding to global financial crises, ultimately reinforcing the stability and resilience of the global economy and societies. In this era of AI-empowered global economic resilience, we possess enhanced capabilities to navigate the intricacies of our interconnected world.