The study introduces NeuroIGN, a groundbreaking multimodal image-guided neurosurgery (IGN) system designed to improve brain tumor surgery outcomes through the integration of deep learning and explainable AI. This novel system is built on a modular architecture that includes features for brain tumor segmentation, patient registration, and explainable output prediction, utilizing open-source packages for an interactive neuronavigational display. The system was rigorously tested in both laboratory and simulated operating room environments, showcasing its precision in tumor segmentation and the effectiveness of ExplainAI in bolstering medical professionals’ confidence in the technology. Notably, NeuroIGN demonstrated a remarkable setup time of 11 minutes in a pre-clinical operating room setting, with a tracking accuracy of 0.5 (± 0.1) mm, and was capable of delivering high frame rates (19 FPS) alongside real-time ultrasound imaging. This research highlights the significant potential of leveraging deep learning and explainable AI to enhance the accuracy and trustworthiness of neuronavigation systems in brain tumor surgeries, potentially leading to better surgical outcomes and improved patient care.