In the field of software development, ensuring the accuracy and quality of code remains a paramount concern. The task of precisely classifying code as correct or incorrect poses inherent challenges. This research introduces a groundbreaking approach that uses clusters constructed from code embeddings generated by CodeBERT to effectively classify code into distinct clusters representing correctness or incorrectness. The model’s ability to grasp intricacies in code semantics and structure leads to a significant reduction in debugging efforts. Consequently, this approach contributes to an overall increase in the reliability and robustness of software systems. CATBOOST algorithm consistently demonstrated high performance with an accuracy of 84% during the experimentation.