Aseptic meningitis is a serious neurological disorder that poses diagnostic challenges due to overlapping symptoms with other conditions and the limitations of conventional diagnostic techniques. Existing approaches often rely on invasive procedures or subjective interpretation of medical images, leading to delays or misdiagnosis. The condition leads to high mortality, especially amongst individuals with Human Immunodeficiency Virus (HIV), and predicting the incidence of disease‑related complications remains challenging, with the value of brain magnetic resonance imaging (MRI) not yet fully explored. To address this, we used a convolutional neural network (CNN) to investigate the complementary contribution of brain MRI to conventional prognostic determinants. A hybrid approach was developed, integrating CNN‑based autonomous feature extraction and recognition with K‑Means clustering for efficient segmentation of medical images. Quantitatively, the proposed model achieved a test AUC of 84.1% ± 2.6, accuracy of 81.3% ± 2.7, F1‑score of 79.2% ± 2.4, and balanced accuracy of 77.3% ± 2.3, consistently outperforming ResNet50, DenseNet121, U‑Net, Vision Transformer, and Swin‑UNet across all evaluation metrics. The framework offers a fast, accurate, and non‑invasive decision‑support tool designed to assist clinicians in the timely identification and management of aseptic meningitis, ultimately improving patient care and outcomes