The objective of this study is to utilize an Artificial Neural Network (ANN) model based on machine learning to forecast employee performance within the Akaki kality sub-city public service and human resource management office. The research methodology employed a cross-sectional descriptive and explanatory survey design with a mixed approach to analyze the relationship between past individual performance and future performance. The focus was on the department of employees, allowing for a straightforward comparison of individual performance. The performance data used in this thesis included annual and half-year efficiency results from the past 11 years, with a sample of 704 employee data for training and testing purposes. The ANN model was trained and validated using employee information records, demonstrating its effectiveness in predicting individual performance in a service delivery context. The study confirmed the applicability of ANN modeling in the public sector, showing that the model could predict 80% of individual performance accurately. Overall, the results of this thesis highlight the potential of using machine learning techniques, specifically ANN models, to optimize employee performance prediction. The findings suggest that such models can be valuable tools for human resource management offices seeking to enhance individual performance within their organizations. The study concluded in June 2023 EC, providing insights into the future application of ANN modeling in service delivery settings.