Accurate forecasting of the Consumer Price Index is crucial for economic stability, enabling policymakers to design effective monetary strategies and mitigate inflationary risks. Traditional models like ARIMA struggle to capture complex non-linear patterns and long-term dependencies in Consumer Price Index data, often leading to suboptimal predictive performance. To address these limitations, this study proposes a hybrid deep learning model combining a Deep Neural Network and a Transformer architecture. The Deep Neural Network extracts non-linear features, while the Transformer’s multi-head attention mechanism captures temporal dependencies, enhancing forecasting precision. The model incorporates advanced preprocessing techniques, including dynamic window normalization and outlier detection, ensuring robust data representation. Experimental results demonstrate superior performance, with a Mean Absolute Error (MAE) of 0.45, Root Mean Square Error (RMSE) of 0.62, and Mean Absolute Percentage Error (MAPE) of 1.8%, outperforming classical benchmarks. Interpretability tools such as SHAP and LIME provide actionable insights, enhancing the model’s transparency and reliability for economic policy-making. This work bridges the gap between econometrics and cutting-edge Artificial Intelligence, offering a scalable solution for Consumer Price Index forecasting.