Forecasting inflation in the presence of changing economic conditions, external shocks, and evolving transmission mechanisms remains an active area of research. This paper applies machine learning (ML) models to forecast headline and core inflation in Tunisia at 1-, 3-, and 6-month horizons, comparing their performance with standard econometric benchmarks within a rolling forecasting framework. Conformal prediction intervals are used to assess forecast uncertainty, while SHAP values serve as an exploratory tool to examine the contribution of explanatory variables to model predictions. The results reveal a clear horizon-dependent pattern, with the predictive gains of ML models increasing at medium and longer horizons. These gains are particularly pronounced at the 6- month horizon, where Support Vector Regression reduces the RMSE by more than 40% relative to the best-performing benchmark. Furthermore, SHAP analysis suggests that the relative contribution of predictors varies across forecasting horizons, with inflation persistence playing a more prominent role at short horizons and monetary, external, and commodity price variables becoming more relevant at longer horizons. Overall, these findings suggest that machine learning methods are most valuable as a complement to, rather than a substitute for, traditional forecasting approaches, particularly at longer horizons where nonlinearities become more pronounced.