Evaluating the productivity of oil wells plays an important role in the effective management of oil production. This process is based on studying the technical and geological characteristics of wells, accurately determining the production potential and formulating effective exploitation strategies. This article analyzes traditional and modern approaches used to assess the productivity of oil wells. The main methods include hydrodynamic studies, geophysical measurements, model-based forecasting and approaches based on artificial intelligence technologies. The advantages and disadvantages of various approaches are compared and their effectiveness in terms of industrial application is examined. Within the framework of this study, the history of development of approaches to assessing the productivity of oil wells, existing methods and prospects are examined. Among the traditional methods, pressure-utility analyses, flow measurements and downhole pressure surveys occupy an important place. In addition, modern approaches include innovative technologies such as artificial intelligence, machine learning and big data analysis. The study shows that modern models and digital technologies allow for more accurate prediction of the production process. In particular, the application of artificial intelligence creates conditions for fast and effective processing of large volumes of data, which optimizes the operational decisionmaking process. At the same time, a comparative analysis of various methods allows determining their suitability in terms of industrial practice. Thus, while traditional methods are in some cases more stable and tested, innovative approaches provide faster and more adaptive results. The results of the study show that the optimal approach is formed using complex methods. For an accurate assessment of the productivity of oil wells, the application of multi-level analysis methods that take into account geological, technological and economic factors is recommended. This approach ensures the sustainable development of oil production, improves strategic planning and resource management. The integration of traditional and modern methods offers a comprehensive view of well productivity, enhancing the accuracy of assessments. Emerging technologies like real-time monitoring and predictive analytics are increasingly being adopted in field operations. These tools help identify underperforming wells quickly and enable timely interventions. Moreover, the growing availability of high-resolution subsurface data further strengthens model-based predictions. As the oil industry continues to digitize, collaboration between data scientists and petroleum engineers becomes crucial for maximizing the effectiveness of these productivity evaluation techniques.