<p>This study investigates the effectiveness of applying advanced scaling techniques using a hybrid Deep Belief Net—Random Forest regressor on production data in unconventional reservoirs such as Multi-Fractured Horizontal Wells. The graphical analysis of the log–log (rates vs. material balance time) plots demonstrates improved clarity in classifying four key flow regimes (ramp-up, linear, transition and BDF) and identifying the time of the end of the transient behavior (end of the line of slope 0.5) using a dataset of 50 wells from D–J Basin of the USA. Acknowledging the reservoir heterogeneity, the curated dataset enhances diagnostic quality and allows for more reliable interpretation of reservoir behavior, with high accuracy metrics of the proposed model. The novelty lies in the integration of DBN as a pre-processor to capture nonlinear correlations in reservoir parameters, such as thickness, compressibility, porosity, lateral length and other crucial characteristics, addressing the specific engineering challenge of automating the end of linear flow regime (<i>t</i>_elf), which is mainly prone to human bias in rate transient analysis. Our DBN-RF model accurately captured fundamental reservoir dynamics with R-square value of 0.76 on the original split, while a cross-validation (with fivefold) reached on average a <i>R</i>² = 0.71 ± SD = 0.04, demonstrating a physics-validated inverse relationship between reservoir thickness and <i>t</i>_elf due to hydraulic diffusivity impact and identifying total compressibility as the primary driver for extending transient production life. We also measure the MAE and RMSE with <i>R</i>² to emphasize robust engineering expectations. Finally, we apply a permutation importance to analyze physical feature drivers of well production. The findings support the development of typical well profiles (TWPs) as predictive tools for future field development, aiming to transition unconventional reservoir management into data-driven and very active strategies.</p>