Social media networks are now an inseparable component of life, serving as a place for information exchange, contact interaction, and audio-visual content. Recommendation systems on these kinds of platforms, like social media platforms, are the key players in this process, helping users get content that they like and are interested in. However, the recommendation systems that are in place these days tend to add an inherent bias; this, in addition to the “cold-start” problem and lack of context awareness, results in poor recommendations and disgruntled users. Here, the object of the research is to tackle the gaps left in the process by suggesting the most effective and relevant strategies for social media recommendation system development. We will conduct an exhaustive literature review to pinpoint the crucial constraints and suggest differentiable options, such as bias mitigation approaches, contextual recommendation models, or even hybrid models, which contain various recommendation strategies. However, the practical implementation and evaluation of these methods are also worth mentioning, the reason being that they may boost recommendation accuracy and user experience. Both the results and the discussion bring forth valuable matters related to the usefulness and future directions of study in this domain.