In the sphere of government administration, the adjudication of service-related cases of government and public sector employees is a complex, crucial and critical process which often influenced by a multitude of factors. The timely disposal of the cases and fair judgment rendered in service matters for government employees is of paramount significance. The sheer volume, technically, and complexity of cases, coupled with the need for consistent and equitable decision-making, makes it an ideal domain for the application of machine learning techniques. This research paper duly addresses the issue through a machine learning-based approach to analyze, classify, and predict the outcome of the judgments in service matters which concerns with public servants. The study leverages a comprehensive dataset comprising historical service matter cases, including diverse attributes such as employee demographics, case details, and predicted outcome of the judgments. Utilizing all the rage machine learning algorithms, including supervised learning for Gaussian Naïve Bayes Classifier, Logistic Regression, K-Nearest Neighbor, Decision Tree Classifier, Support Vector Classifier (SVC), Linear Support Vector Classifier (LSVC), Random Forest Classifier, artificial neural networks and also Natural Language Processing (NLP) meaningful patterns, relationships, and insights from the dataset can be extracted and put in use. Furthermore, the research explores the predictive aspect of machine learning by developing models capable of forecasting potential outcomes for pending service matters, whose disposal is of paramount significance as the public servants are continuously suffering for a prolonged period of time. These predictive models take into account the specifics of each case, historical data, and contextual information to generate and render the probabilistic judgments as per the facts and circumstances of the case. The results of this research help for the speedy disposal of sensitive cases. By utilizing the potential of machine learning, government agencies can make informed decisions, allocate resources efficiently, and reduce the backlog of pending cases. Moreover, this approach contributes to a fairer and more consistent adjudication process, ultimately benefiting government employees and enhancing overall administrative effectiveness. This study contributes to the ongoing efforts to integrate data-driven approaches into the legal and administrative domains, promoting fairness, consistency, and effectiveness in handling service matter disputes for public servants.