Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics, used to identify homologous regions across biological sequences, providing insights into evolutionary relationships and protein functionality. When applied to transmembrane proteins (TMPs), however, the MSA problem becomes notably more complex due to the unique structural and evolutionary constraints imposed by the lipid bilayer. In this paper, we present a systematic review of the current MSA tools and algorithms specifically designed for TMPs. We applied the guidelines proposed by Barbara Kitchenham for conducting systematic literature reviews. Through an analysis of five key tools STAM, AlignMe, PRALINE \(^{TM}\) , TM-Coffee, and TM-Aligner, we evaluate their methodologies, substitution matrices, transmembrane region prediction techniques, and scoring functions. Our results indicate that transmembrane-specific substitution matrices, such as PHAT, significantly improve alignment accuracy in transmembrane regions. Additionally, tools that incorporate topology predictions, such as TMHMM or HMMTOP, play a vital role in preserving the structural integrity of TMPs during alignment. We highlight the strengths and limitations of each tool: TM-Coffee excels in alignment accuracy but is computationally expensive, while TM-Aligner offers a balanced solution between speed and accuracy for large datasets. AlignMe and PRALINE \(^{TM}\) provide users with flexible approaches that integrate additional structural and evolutionary data, enhancing alignment quality for TMPs. This review provides valuable guidance for researchers selecting MSA tools for TMPs and suggests future directions for improving alignment methods for these challenging protein types.