Despite significant improvement in education levels within Cambodia over recent decades, high school failure rates remain a persistent concern. A quick intervention through the usage of digital transformation is needed. Business Intelligence (BI) and Educational Data Mining (EDM), which offer powerful tools for extracting valuable insights from raw data, hold notable potential to address this challenge within the education domain. This research investigates the application of BI/EDM techniques to predict student achievement in high school students and analyzes effective features to help high school students reach their full potential. Data encompassing recent real-world factors such as student grades, demographics, socio-economic and school-related features was gathered through questionnaires. Most input features were modeled under various binary/five-level classification tasks. Additionally, eight data mining models (Multinomial Naïve Bayes, Decision Tree, Bootstrap Aggregating, Stochastic Gradient Descent, K-Nearest Neighbors, Random Forest, Support Vector Machines, and Neural Networks) were tested, alongside different input selection strategies (including and excluding semester grade). Evaluation metrics like Accuracy, Recall, Precision, and F-measure were used to assess models. The results demonstrate that highly accurate predictions of student achievement are achievable, particularly when incorporating grades from the first semester. Neural Network achieved the best result with an Accuracy of 93.40%, Precision of 95.45%, Recall of 96.55%, and F-Measure of 96.00%. Apriori algorithm is used to identify the relevant features and make a recommendation to the high school student based on the predicted result. While past performance emerges as a notable predictor, the explanatory analysis reveals the influence of other relevant features, including parental background, parent support, parent motivation, self-learning, self-motivation, learning environment, and even student health. This research led to the development of a more efficient analysis and reporting service that will streamline data analysis within the Learning Management System (LMS). It has the potential to significantly improve the quality of education and optimize educational resource management.