In brain cancer surgery, maximal tumor resection improves overall survival and quality of life survival in low-grade and high-grade glioma. Different technologies such as intraoperative magnetic resonance imaging and computed tomography have made major contributions; however, these technologies do not provide quantitative, real-time and three-dimensional continuous guidance. Optical Coherence Tomography (OCT) is a non-invasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here we report a novel Artificial Neural Network (ANN)-based computer-aided diagnosis (CAD) method for automated, real-time, in situ detection of glioma-infiltrated tumor margins. Near 500 volumetric OCT samples were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either non-cancerous or glioma-infiltrated based on histopathology evaluation (gold standard). Labeled OCT images from 12 patients were used as training dataset to develop the artificial neural network. Unlabeled OCT images from the other 9 patients were used as a validation dataset to quantify the method detection performance. The CAD system achieved excellent levels of both sensitivity and specificity (∼90%) for detecting glioma-infiltrated tissue with high spatial resolution (∼16 μm laterally). Previous methods for OCT-based detection of glioma-infiltrated brain tissue rely on underlying optical properties such as attenuation coefficient from the OCT signal requiring sacrificing spatial resolution and cumbersome calibration procedures. By overcoming these major challenges, our novel ANN-assisted CAD system will enable implementing practical OCT-guided surgical tools for continuous, real-time and accurate intra-operative detection of glioma-infiltrated brain tissue, facilitating maximal glioma resection and superior surgical outcomes for glioma patients.