The generation of source code via Artificial Intelligence has become a prevalent practice in both academia and industry, posing significant challenges to academic integrity and authorship attribution. In this work, we address SemEval-2026 Task 13: Detecting Machine-Generated Code by evaluating the effectiveness of four distinct methodologies: Graph Neural Networks (GNNs), Pre-trained Language Models (PLMs), Large Language Models (LLMs), and Stylometric Feature Engineering using XGBoost. Our approach focuses on three specific scenarios: Subtask A (Binary Detection), Subtask B (Multi-Class Authorship), and Subtask C (Hybrid Code Detection). While our models achieved high performance during the validation phase, the transition to the final test set revealed substantial challenges in generalization, likely due to the increased diversity of programming languages and generators in the unseen data. This work serves as a foundational first step, identifying critical gaps in model robustness and highlighting the need for more sophisticated methodologies to bridge the performance gap in complex, real-world environments.