Electrical submersible pumps (ESPs) are one of the most significant artificial lifts used worldwide. However, the capital and operating costs of such equipment are higher than those of other artificial lift (AL) methods, and extensive work has been done to increase their reliability. The ESPs' reliability is mainly expressed through run-life metrics such as Mean Time Between Failure (MTBF), where significant research has been made to develop methodologies to calculate and interpret run-life metrics and eventually improve designs and operational efficiency. Dominantly, the ESP run life depends on the design and operating conditions, and there are still significant challenges that these systems are experiencing, especially in harsh environmental conditions where extensive gas, solids, and water production can deteriorate pump run life. To tackle these challenges and continuously improve ESP design, the industry has adopted a culture of continuous improvement practices reflected through failure analysis. Since ESPs can fail due to multiple reasons, failure analysis and identifying the failure's root cause are critical in improving future designs. In general, failure analysis can be divided into three types: detection, isolation, and prognosis. Failure detection is simply recognizing that the failure occurred without acting or knowing the root cause. Failure isolation identifies the root cause of failure, while prognosis refers to predicting the time the equipment will fail. Failure analysis concludes when the exact root cause of failure is determined, and data and captured lessons are documented, which in turn improves workover planning and helps improve the future design in the long run. Understanding the main drivers and factors affecting ESP performance is essential to improve system reliability continuously. In addition, to run life metrics, the industry has been using several statistical models, such as survival analysis, to estimate and compare run life metrics better. However, these statistical models only include operational data generated through part of the well life cycle (e.g., well events) to predict the run life effectively. This Thesis will investigate available solutions data models (databases) to acquire failure and operational data, and it will explore the statistical model to estimate the pump's remaining run life through slow feature analysis (SFA).