Inflation plays a very pivotal role in overall decision-making within an economy, as it carries significant implications for different macro- and microeconomic indicators within an economy. To use the inflation effectively as a decision-making tool, it becomes important for policymakers to predict the inflation efficiently and timely. The existing literature has largely relied on standard statistical measures for estimation of inflation. The present study tries to apply different machine learning models to predict the inflation in the Indian economy. The study considers monthly time-series data of inflation, exchange rate, stock market returns, repo rate, index of industrial production, bond yield rate, and money supply for a sample period of October 2017–October 2023. The result of the study confirms that in the Indian economy, the XGBoost model outperforms all other machine learning and linear regression models. The second best performer is Random Forest, followed by linear regression. These models can help policymakers and researchers reach the most reliable forecasts of inflation in the Indian economy.