<p>The exponential growth of digital technology and the widespread use of web applications have led to an overwhelming increase in data, necessitating effective recommender systems to filter and present relevant content. Recommender systems have evolved as essential tools in the digital era, providing personalized suggestions by analyzing user interests and behavior patterns. Integration of these systems has become an essential component of digital marketing strategies. They hold significant positions in a wide range of domains, including streaming services (music, movies, and books), social media systems, digital governance, electronic commerce, e-libraries, e-learning systems, traveling and resource services, and many more. Recently, recommender systems have been extensively employed in the healthcare and educational sectors to assist users in identifying and accessing content that aligns with their interests. While these systems offer numerous advantages, they are also confronted with several formidable challenges, including the cold-start problem, data sparsity, the presence of gray sheep users, starvation, and shilling, which can negatively impact their performance. Extensive research has been conducted to address these challenges and improve the accuracy of recommender systems. This review provides a comprehensive overview of the primary methods, including Content-Based Filtering, Collaborative Filtering, Knowledge-Based Systems, Demographic-Based Systems, Community-Based Systems, Hybrid Systems, Cross Domain Systems, and Social Recommender Systems, along with their respective merits and demerits. It also examines the evaluation methods and metrics used to assess recommender systems performance. Additionally, the review explores the application of recommender systems across diverse domains and highlights the tools and technologies supporting their development. Future opportunities and unresolved issues in recommender systems are explored to help promote continued research and innovation.</p>