<p>This study introduces a novel composite approach to nonlinear inflation dynamics in identifying historical inflation patterns and forecasting future regime shifts. Assuming inflation’s responsiveness to its determinants varies across inflation regimes and that inflation shock magnitude shapes the dynamics, we endogenously identify distinct inflation regimes and analyze nonlinear behaviors within such regimes for the BRICS countries (Brazil, Russia, India, China, and South Africa) and Türkiye. In the first stage of our analysis, we employ a Hidden Markov Regime Switching Model combined with Monte Carlo simulations to establish high- and low-inflation thresholds. In the second stage, we utilize an ordered probit model to identify nonlinear probabilistic relationships between inflation regimes and key drivers of inflation such as unit labor costs, exchange rates, and global inflation. Our method achieves over 90% accuracy in predicting inflation regimes based on historical data. It also shows particularly strong out-of-sample performance in the post-pandemic period, outperforming the forecasts of international financial institutions. Even without prior knowledge of exogenous variables, the method anticipates regime shifts in five of the six countries analyzed for 2022 and 2023. Our approach offers researchers and central bankers a robust alternative analytical framework for managing high- and low-inflation environments where traditional linear or equilibrium-based models fall short.</p>