<p>Reservoir heterogeneity in geologically complex settings poses significant challenges for accurate characterization and predictive modeling, especially in hydrocarbon-rich regions such as the eastern Sirte Basin, Libya. This study develops a robust workflow that combines Artificial Neural Networks (ANNs) and Self-Organizing Maps&#xa0;(SOMs) to enhance the prediction of porosity and permeability while integrating computational outputs with geological insights. The methodology utilizes an extensive dataset from twenty-nine wells, comprising 3,417 core plugs, 2,945 core descriptions, wireline logs, and detailed chemostratigraphic data, addressing the limitations of traditional regression models constrained by linear assumptions. Traditional regression methods, limited by their inability to model nonlinear relationships, yielded correlation coefficients R² of 0.33 and 0.24 for porosity and permeability predictions, respectively. ANNs demonstrated significantly superior predictive performance, achieving R² values of 0.89 for porosity and 0.85 for permeability, coupled with minimal bias and robust error distributions. Complementing this, SOM clustering delineated depositional facies and stratigraphic controls, effectively linking machine learning outputs with practical geological interpretations. This integrated approach bridges computational precision and geological understanding, offering a scalable framework applicable to diverse geological settings worldwide. The study’s findings underscore the potential of combining advanced machine learning techniques with core and log data to optimize hydrocarbon recovery strategies and address reservoir heterogeneity. By leveraging these methodologies, this workflow establishes a new standard for reservoir characterization and resource management in geologically complex basins.</p>