This study introduces a novel two-step machine learning methodology to generate high-frequency (daily and weekly) inflation forecasts in developing economies, where official statistics are typically available only at a monthly frequency and with delays. High-frequency forecasting here is interpreted as nowcasting or interpolation — real-time prediction ahead of official releases or within-period estimation using mixed-frequency indicators — while also serving as a data-augmentation strategy. In the first step, high-frequency predictors are aggregated to construct monthly-aligned features that serve as inputs for training machine learning models. In the second step, various feature selection techniques are evaluated and multiple machine learning algorithms are rigorously fine-tuned via hyperparameter optimization. Through systematic evaluation, a final model was selected — Ridge regression trained on an L1-regularized feature subset — that achieves superior out-of-sample accuracy. This model is then deployed to produce high-frequency year-on-year CPI inflation nowcasts. Forecasts exhibit strong temporal alignment with observed monthly values, while distributional equivalence — monthly vs. high-frequency projections — is confirmed via Kolmogorov–Smirnov tests. Compared to benchmark econometric models, the proposed approach delivers improved predictive performance, offering timely and granular insights for forward-looking monetary policy.