The Red Deer algorithm (RDA), a recent population-based meta-heuristic algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest principle from the evolutionary algorithms and the productivity and richness of heuristic search techniques. Different variants and hybrids of this algorithm are presented and investigated. All the applications that were solved with this algorithm are presented. It is crucial to analyze the performance of this algorithm, therefore, the paper sheds light on the algorithm unique features and weaknesses covering the applications that are primarily suitable for it. The conclusions are presented, and further recommendations are suggested based on the review and analysis covered. The readers of this paper will have an understanding of the RD algorithm and its variants and, consequently, decide how suitable this algorithm is for their own business, research, or industrial applications.