Artificial intelligence (AI) has emerged as a transformative tool in mental health care, offering predictive capabilities that can identify individuals at heightened risk of crisis before acute episodes occur. By integrating diverse data sources such as electronic health records, wearable device metrics, social media activity, and patient self-reports AI-driven models can enhance early intervention strategies, reduce hospitalization rates, and improve care outcomes. However, the implementation of these predictive systems in clinical decision-making raises critical ethical, legal, and social considerations. Privacy remains a primary concern, as sensitive mental health data is highly vulnerable to breaches and misuse, requiring robust technical safeguards such as differential privacy, federated learning, and secure multiparty computation. Informed consent presents another challenge, as patients must fully understand the implications of AI-driven risk assessment, including potential consequences for treatment access, insurance coverage, and personal autonomy. Furthermore, fairness in AI prediction models is essential to avoid reinforcing existing health inequities particularly those related to socioeconomic status, race, gender, and cultural background through biased training datasets or opaque algorithmic processes. This paper examines the intersection of AI’s technical potential and the ethical imperatives of mental health crisis prediction. It proposes a framework that balances predictive accuracy with transparency, inclusivity, and respect for patient rights. Strategies include co-designing algorithms with diverse stakeholder input, conducting regular bias audits, and implementing explainable AI tools to support clinician-patient discussions. By navigating the complex interplay of privacy, consent, and fairness, AI can responsibly augment clinical decision-making, contributing to more equitable, anticipatory, and patient-centered mental health care systems.