This chapter explores the potential of machine learning and big data analytics in bipolar disorder research. We aimed to clarify terminology, describe existing challenges, and highlight potential applications relevant to bipolar disorder. Big data analytics holds promise for developing risk calculators to guide treatment decisions and predict clinical outcomes, including suicide risk, for individual patients. This data-driven approach could revolutionize diagnosis by enabling the identification of more relevant phenotypes and predicting the transition to bipolar disorder in high-risk individuals. However, we also discuss the significant challenges facing big data applications in bipolar disorder research, including data heterogeneity, limited external validation and replication of findings, cost considerations, nonstationary data distributions, and inadequate funding. Machine learning, including atheoretical data-driven big data approaches, offers an exciting opportunity to improve risk detection, identify relevant phenotypes, and inform treatment selection and prognosis in bipolar disorder. However, overcoming methodological hurdles remains crucial for translating research findings into real-world clinical practice. Beyond this, digital phenotyping and quantum computing are promising fields for significant advancements in the coming years, offering exciting possibilities for breakthroughs in mental health research.