Brain tumors represent a significant global health concern, where precise and timely diagnosis is crucial for effective treatment and patient prognosis. Current reliance on manual interpretation of Magnetic Resonance Imaging (MRI) can be time-intensive, subject to inter-observer variability, and susceptible to diagnostic errors. This is particularly evident in differentiating various tumor types and in assessing glioma malignancy, the latter often necessitating invasive biopsy procedures which carry inherent risks. This project, NeuroDetect, addresses these diagnostic challenges by developing and evaluating a sophisticated dual-component computational system. The first component targets multi-class brain tumor characterization (Glioma, Meningioma, Pituitary, No Tumor). It employs an ensemble approach, integrating features from an established convolutional network (ResNet50) with a bespoke network architecture enhanced by refined feature selection mechanisms, achieving approximately 98.55% accuracy on a public Kaggle MRI dataset. The second component focuses on non-invasive glioma grade differentiation (Low-Grade vs. High-Grade). A novel hybrid computational architecture was developed, which synergistically combines information extracted from two distinct pre-trained image analysis backbones (ResNetV2 and EfficientNetB0). This is further augmented by a targeted feature-weighting mechanism applied after information fusion from 2D axial FLAIR and T1ce MRI slices (BraTS 2019 dataset), yielding approximately 91.86% accuracy and 0.83 AUC. NeuroDetect is prototyped as a user-friendly mobile application, demonstrating the potential for advanced computational tools to provide accessible, accurate, and rapid decision support for clinicians, with the aim of improving diagnostic pathways and patient care.