Brain tumor is categorized as one of the most fatal form of cancer due to its location and difficulty in terms of diagnostics. Medical expert relies on two key approaches which include biopsy and MRI. However, these techniques have several setbacks which include the need of medical experts, inaccuracy, miss-diagnosis as a result of anxiety or workload which may lead to patient morbidity and mortality. This opens a gap for the need of precise diagnosis and staging to guide appropriate clinical decisions. In this study, we proposed the application of deep learning (DL)-based techniques for the classification of MRI vs non-MRI and tumor vs no tumor. In order to accurately discriminate between classes, we acquired brain tumor multimodal image (CT and MRI) datasets, which comprises of 9616 MRI and CT scans in which 8000 are selected for discrimination between MRI and non-MRI and 4000 for the discrimination between tumor and no tumor cases. The acquired images undergo image pre-processing, data split, data augmentation and model training. The images are trained using 4 DL networks which include MobileNetV2, ResNet, Ineptionv3 and VGG16. Performance evaluation of the DL architectures and comparative analysis has shown that pre-trained MobileNetV2 achieved the best result across all metrics with 99.94% accuracy for the discrimination between MRI and non-MRI and 99.00% for the discrimination between tumor and no tumor. Moreover, I-BrainNet which is a DL/IoT-based framework is developed for the real-time classification of brain tumor.