Primary healthcare serves as the foundation of health systems, providing accessible, preventive, and continuous care for everyday needs, especially in managing chronic conditions and promoting community well-being. However, the COVID-19 pandemic exposed critical weaknesses such as information gaps, supply shortages, and fragmented health IT systems. Artificial intelligence (AI) offers transformative potential to address these gaps—enhancing disease forecasting, supporting remote care, and optimizing resource allocation—though ethical concerns around privacy, fairness, and inclusivity must be addressed to ensure safe and equitable implementation. This chapter explores how AI can transform primary healthcare, particularly in resource-limited and disaster-prone settings, by enhancing robustness, fairness, and transparency. It also addresses key challenges such as model generalizability, data shift, and ethical deployment, highlighting the importance of clinician co-design and interpretable, bias-mitigated approaches for sustainable impact. To help readers recognize the tremendous potential of AI and its advantages over human capabilities in processing and analysing healthcare data. At the same time, critical challenges are highlighted—particularly the inherent biases stemming from training data and the lack of complete human control over AI behaviour. Building AI tools for primary healthcare should not focus solely on performance metrics; it is equally, or even more important that these systems are understandable, transparent, and act responsibly. This chapter provides a clear understanding of the key issues in AI development and inspires readers to contribute to the responsible design, evaluation, and application of AI in primary healthcare settings.