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Pharmaceutical Quality Assurance (QA) is fundamental to ensuring the safety, efficacy, and regulatory compliance of medicinal products throughout their lifecycle. The rapid advancement of artificial intelligence (AI) and automation technologies under the paradigm of Pharma 4.0 has significantly transformed traditional QA systems, enabling a shift from reactive quality control to predictive and preventive quality management. This review critically examines the evolving role of AI and automation in pharmaceutical quality assurance, with emphasis on their applications in manufacturing oversight, in-process quality control, visual inspection, data integrity, and regulatory compliance. AI-driven tools such as machine learning, computer vision, natural language processing, predictive analytics, and anomaly detection enhance real-time monitoring, defect identification, root-cause analysis, and decision-making accuracy while minimizing human error and operational variability. Automation technologies including robotic process automation, electronic batch records, manufacturing execution systems, laboratory information management systems, and digital validation platforms further strengthen compliance with Good Manufacturing Practices (GMP), GxP requirements, and global regulatory frameworks such as FDA and EMA guidelines. The review also addresses key challenges associated with AI adoption, including data quality and scarcity, algorithmic bias, system interpretability, cybersecurity risks, infrastructure limitations, workforce skill gaps, and regulatory complexity. Emerging solutions such as explainable AI, digital twins, IoT integration, and human-AI collaboration are discussed as critical enablers for sustainable implementation. Overall, AI and automation represent transformative tools for modern pharmaceutical QA, offering enhanced efficiency, reliability, and compliance while supporting continuous quality improvement and accelerated time-to-market. Their responsible and validated integration is essential for ensuring patient safety and maintaining regulatory confidence in increasingly complex pharmaceutical systems. Keywords: Pharmaceutical Quality Assurance; Artificial Intelligence; Automation; Pharma 4.0; Machine Learning; Predictive Analytics; Regulatory Compliance; Digital Twin