Suicide has become a global health crisis driven by a complex mix of social, environmental, and psychological factors. The prevalence of suicide has emerged as a worldwide health crisis, presenting a multitude of intricate issues. The correlation between increased concerns about sustainability, climate change, and ecological degradation and the subsequent burden on psychological health is an increasing concern. Although Suicidal Ideation Detection (SID) has been effectively addressed by prior research using Machine Learning (ML) and Deep Learning (DL) models, many studies fail to handle the complexities of real-time detection. To overcome these flaws, we developed an novel system called Multimodal Visual interactive system for Suicide Ideation Detection (MVis4SID), which utilizes visual insights and real-time data to provide a more comprehensive exploration of suicidal thoughts. MVis4SID employs multimodal aspects, including click or touch, text, and voice, to visualize the performance metrics for suicidal ideation using ML and DL models; thus, offering a more holistic solution to this escalating problem. The rigorous evaluation of the user study shows that MVis4SID not only outperforms existing methods in accuracy, but also explains the responsible use of the underlying AI approach, demonstrating its potential to improve SID and intervention strategies. In addition, it provides a user-friendly interface for decision-makers, allowing them to represent the model performance and the efficient visual evaluation.