The readiness of students for the workforce is a critical metric in evaluating the quality of higher education. Forecasting students' work readiness before their entrance into the industry calls for a data-driven approach since they often lack the necessary skills and experience until graduation. Using two machine learning techniques—Artificial Neural Network (ANN) and Decision Tree (DT)—this research aims to create a classification model to predict students' employment readiness. Among the several aspects the data covers are academic knowledge, professional attitude, soft skills, hard skills, and socio-economic background. Data preparation, data cleansing, feature selection, model training, and performance evaluation make up the study approach. The ANN model comprised four hidden layers, while the DT was refined with RandomizedSearchCV. The test results showed that DT had an accuracy of 90.80%, and ANN had 90.69%, indicating that both performed very well and can be selected based on what the user needs most. This research contributes a predictive model for educational institutions to assess students' employment preparedness in a more objective and systematic way.