Enhanced Oil Recovery (EOR) techniques have gained significant attention in improving hydrocarbon production in reservoirs with challenging conditions. This study focuses on screening methodologies that can effectively assess the potential of various EOR methods in complex reservoir environments, including heterogeneous, depleted, and highly viscous systems. The aim is to enhance production forecasting outcomes by integrating multi-faceted screening approaches. These methodologies incorporate a combination of analytical, experimental, and numerical techniques to evaluate the feasibility, efficiency, and long-term sustainability of different EOR methods such as thermal recovery, gas injection, chemical flooding, and microbial EOR. Through a systematic review of existing screening models, this study identifies key parameters that influence the performance of EOR in challenging reservoirs, such as rock-fluid interactions, pressure, temperature, and reservoir heterogeneity. Furthermore, the paper discusses the importance of incorporating real-time data analytics and reservoir simulation tools in refining production predictions. By utilizing sophisticated reservoir models, it is possible to simulate EOR processes under various operating conditions, improving the accuracy of forecasts and reducing uncertainty in production estimates. The study also highlights the importance of a tailored approach to EOR screening that considers both technical and economic factors, emphasizing the need for cost-benefit analyses to ensure the most efficient use of resources. The application of advanced machine learning algorithms and optimization techniques is explored as a potential means of improving the decision-making process for EOR deployment. Finally, the paper outlines several case studies where advanced screening methodologies have led to successful application of EOR techniques, resulting in optimized recovery rates in difficult-to-manage reservoir environments.