Suicide remains a critical global public-health concern, particularly in developing and underdeveloped regions where mental-health infrastructure is limited. Early detection and timely intervention are essential, yet traditional suicide-risk assessment methods often fail to capture subtle warning signs due to stigma, underreporting, and heavy clinical workloads. Multimodal artificial intelligence (AI) offers a promising solution to these gaps by providing fast, quantifiable, individualized, and data-driven risk prediction. Multimodal AI integrates machine learning (ML), deep learning (DL), and natural language processing (NLP) to analyze diverse inputs that reflect a patient’s mental state. ML models such as support vector machines and random forests effectively process structured data, including electronic health records and psychometric assessments, identifying patterns that may be overlooked in routine evaluations. DL architectures—including long short-term memory networks and transformer-based models like Bio_ClinicalBERT—enable the interpretation of unstructured information from speech, facial expressions, clinical notes, and social media behavior associated with suicidality. NLP further enhances accuracy by conducting sentiment, semantic, and syntactic analyses to detect linguistic markers such as self-referential thinking, hopelessness, worthlessness, and flattened affect. By combining insights from multiple data modalities, AI systems can generate a more holistic and objective assessment of suicide risk, supporting clinicians in early identification and targeted intervention. However, significant challenges remain, including cultural variability in suicide expression, risks of algorithmic overgeneralization, data-privacy concerns, and insufficient AI literacy among healthcare professionals. Effective and ethical implementation requires clinician oversight, culturally sensitive model development, and integration of AI tools into clinical training. This chapter highlights the potential of multimodal AI to shift suicide prevention toward a proactive, precise, and evidence-based approach, ultimately improving mental-health outcomes and reducing global suicide burden.