Content-based recommendation systems have become essential for improving user experiences in e-commerce and various digital platforms. This review paper examines the recent advancements in content-based recommendation systems, focusing on machine learning techniques and models used to personalise user interactions. The paper also explores the role of deep learning and hybrid approaches in increasing the accuracy and relevance of recommendations. Despite significant progress, the product recommendation systems face challenges such as capturing complex user preferences, ensuring scalability, addressing the cold start problem, and improving explainability which remains crucial and requires further research. This paper offers a comprehensive overview of current methodologies, identifies existing limitations, and suggests future directions to optimise content-based recommendation systems to provide more effective and reliable recommendations.