Particularly affecting patient response to alkylating treatment, the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a well-established prognostic and predictive biomarker in gliomas. Conventional evaluation techniques are prone to limits including sampling mistakes and intratumoral heterogeneity and call for invasive tissue biopsies. In this work, we present a non-invasive, deep learning-based system for multi-modal magnetic resonance imaging (MRI) based MGMT promoter methylation prediction. The method combines improved preprocessing, automated tumor segmentation, and a customized EfficientNet-based classification architecture with structural MRI sequences including T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR imaging. Our model achieves strong performance, high accuracy and generalizability in methylation status prediction. Comparative study including current literature shows either better or equivalent prediction performance, so highlighting the clinical possibilities of this technique. The suggested pipeline advances the function of virtual biopsy in neuro-oncology by providing a scalable, dependable, radiation-free substitute for MGMT methylation testing, therefore enabling individualized therapy planning.