Today, many industries such as e-commerce websites, social media sites, streaming services, news platforms heavily rely on recommendation systems (RSs). All these industries are generating vast amounts of data resulting in the problem of data overloading. Recommendation system aims to improve user experience by providing tailored and pertinent recommendations. The accuracy and applicability of recommendations must be continuously assessed and improved upon throughout time. Nonetheless, these systems come with built-in difficulties such as sparsity, the cold start issue, the rise of gray sheep users, and many more. The sparsity issue emerges from the recommendation dataset’s restricted availability of clear user-item interactions. In these kinds of situations, recommendation systems, especially traditional collaborative filtering techniques, perform poorly and produce predictions that are not up to the mark. To increase the precision and potency of recommendation algorithms, this review paper serves as a valuable resource for researchers, practitioners, and to understand the data sparsity problem in recommendation systems by discussing various techniques and providing valuable conclusions and future directions.