This paper examined the dual objectives of fostering innovation through AI while ensuring robust data privacy. The paper adopted the doctrinal method of research by relying on both primary and secondary sources of information or data. Primarily, it utilises a legal approach using data such as the European Union Artificial Intelligence Act, the General Data Protection Regulation, the California Consumer Protection Act and the Nigeria Data Protection Act. The secondary sources of data used include textbooks, online articles in learned journals, relevant materials from the internet, magazines, newspapers, other periodicals, dictionaries and reports. The study found that a multi-faceted approach is necessary to navigate the intersection of AI and data privacy, ensuring that technological progress does not compromise individual rights and trust in AI systems and finally make recommendations which include technical solutions such as differential privacy, federated learning, and homomorphic encryption which aim to balance data utility and privacy