Forecasting business cycles and macroeconomic trends is inherently challenging due to their complex and non-linear relationships with volatile and noisy economic factors. However, the growing availability of large-scale economic data, coupled with advances in computational power, creates new opportunities to extract meaningful information and develop robust predictive models. Deep learning methods are well suited for handling noisy and complex data; nevertheless, their application to business cycle prediction remains at an early stage. To address this gap, this study develops an end-to-end computational framework that implements state-of-art deep learning architectures for identifying business cycle phases. The key contributions of this work include systematic input feature selection across a broad range of economic sectors, advanced preprocessing techniques for noisy data, the development of a customizable and reproducible computational framework, a data-driven approach to hyperparameter tuning, and the use of robust model selection strategies. Twenty two models based on two architectures—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—were implemented and demonstrated strong predictive performance, highlighting the effectiveness of the proposed approach for forecasting macroeconomic trends. Among these, the GRU model with 512 neurons achieved the best results, with an accuracy of 86.42%, precision of 92.03%, recall of 93.32%, and an F1-score of 92.34%. Overall, the findings provide valuable insights that can support informed decision-making by policymakers and other stakeholders.