This study aims to integrate ultrasound tomography with numerical algorithms to significantly enhance brain sensing capabilities for diagnosing critical brain abnormalities. Advanced ultrasound tomography, employing a high-frequency transducer array, captures intricate brain structures. The echoes processed by multi-channel receivers allow for three-dimensional imaging. Deep learning models, particularly convolutional neural networks, undergo rigorous training on extensive datasets. Hyperparameter tuning and regularization are key to model optimization. Algorithms handle large datasets, detecting subtle pathological changes in ultrasound images. The system demonstrates proficient image reconstruction and analysis. Implementing deep learning algorithms rectifies operator-dependent inconsistencies and imaging artifacts. The analysis shows significant improvements in diagnostic accuracy and processing time. The convergence of ultrasound tomography and deep learning faces challenges such as image quality variation, computational demands, and clinical integration. Despite these, the enhanced image clarity and the ability to conduct real-time analytics are promising. The study sets a new standard in neurological diagnostics, indicating the potential for sophisticated diagnostic tools to become accessible in diverse healthcare settings.