The Beam Pump (BP) or the Sucker Rod pump is used for low, medium to high production rates, the most effective and dependable artificial lift technique. Even though BP has a low to medium capital cost, the production losses brought on by its failure are significantly higher. Machine learning (ML) is a subfield of artificial intelligence that has obtained significant interest in the oil and gas sector due to its capacity for prediction. The goal of this research is to create an ML model that can forecast BP failure and pinpoint the critical elements that affect it. A general review is conducted for the BP failure analysis approach, monitor, troubleshoot and predict its failure. The study explores various ML algorithms and their applications in predicting the failure type of BP in different fields around the world. The aims of this thesis were directed through BP static and historical data to be achieved based on the literature review. The failure history of around 573 wells from an Omani oilfield was reviewed, all of the wells are BP failures attributed to sand, mechanical failure and scale accumulation. The dataset includes static features and dynamic features related to BP performance, design, installation, commissioning, failure, pull-out and teardown. Data split into training and testing sets by a ratio of 70:30. Tests were performed to three selected algorithms to optimize the parameters, based on accuracy, precision, and true positive rate. The algorithms tested were Decision Tree (DT), Random Forest (RF), and K Nearest Neighbors (KNN). After determining that the final model's accuracy was very high and promising, it was decided to deploy and implement the idea into TIBCO Spotfire, a visualization tool that offers various ideas to predict model failure. However, human intervention will be required to adjust the parameters in accordance with the high risk ranking whenever the tool provides us with a high rank, allowing us to work proactively and prevent any pump failures. We were able to lower the number of failures in a year thanks to the developed Spotfire tool, and our goal going forward is to lower the number of failures even further to cost on failure maintenance, new pump installations, hoist intervention, and down oil lost per barrel. The pump fillage, minimum load, and peak load are the most crucial parameters to keep an eye on and regulate. Through human intervention and the scheduling of preventive and corrective maintenance for BP wells, we can minimize and eliminate failures and take proactive measures to identify and isolate high-risk wells.