The process of neurosurgery is delicate and time consuming, often resulting in lower patient outcomes and survival rates. Therefore, we are implementing computer algorithms for the classification and segmentation of brain tumors. This aims to improve the accuracy of identifying tumor and segmentation of the brain. The automatic segmentation of the brain is based on CT (Computed tomography) data using the Desktop Image Processing System (DISOS). The DISOS system integrates modules such as preoperative planning and user guidance. Automated segmentation algorithms, including fuzzy c means clustering method, intensity thresholding, region growing, level set methods, and machine learning approaches, are employed to segment the brain structures. This system demonstrates improved accuracy, robustness, and efficiency, although a final check by a medical expert is still necessary. Additionally, our paper highlights the use of advanced algorithms and machine learning techniques to differentiate between cancerous and non-cancerous tumors using various medical imaging data. The incorporation of genetic, molecular, and clinical data further enhances the accuracy of tumor classification models. Challenges in real time robot path planning and navigation for neurosurgery are addressed, including the use of A-mode ultrasound and augmented reality systems to provide enhanced guidance during procedures. This study concludes by emphasizing the collaboration between medical professionals and computer scientists in advancing the field of computer-assisted brain tumor diagnosis and robot-assisted surgery. Our goal is not to replace humans with robots, but the collaboration between them for incredible outcomes.