A systematic literature review (SLR) was conducted to evaluate indirect treatment comparison (ITC) methods used in health technology assessment (HTA) to provide guidance on the most appropriate techniques for different scenarios. The review identified 73 articles reporting on seven ITC techniques: network meta-analysis (NMA), matching-adjusted indirect comparison (MAIC), network meta-regression (NMR), the Bucher method, simulated treatment comparison (STC), propensity score matching (PSM), and inverse probability of treatment weighting (IPTW). NMA was the most frequently described technique (79.5%), followed by MAIC (30.1%), NMR (24.7%), the Bucher method (23.3%), STC (21.9%), PSM (4.1%), and IPTW (4.1%). MAIC and STC were common in single-arm studies, while NMA and the Bucher method were suitable when individual patient-level data (IPD) were unavailable. ITC techniques aim to provide alternative evidence when direct comparisons are not possible. However, their acceptability remains low, and clearer international consensus is needed to improve their quality. ITC techniques are evolving rapidly, and more efficient methods may become available in the future. The review highlights that all ITC techniques are forms of adjusted indirect comparison. NMA and the Bucher method assume constant relative treatment effects, which may lead to biased estimates if cross-trial differences exist. MAIC and STC are population-adjusted methods that adjust for treatment effect modifiers (TEMs) to ensure comparability between populations. These methods are more complex and time-consuming than NMA but offer better accuracy in cases of unbalanced TEMs. PS-based techniques, such as PSM and PSW, use propensity scores to balance covariates and reduce selection bias in observational studies. However, they rely on the assumption that all relevant TEMs have been adjusted for and may be biased if unobserved variables are present. The review emphasizes the importance of selecting the appropriate ITC technique based on the feasibility of a connected network, the evidence of heterogeneity between and within studies, the number of relevant studies, and the availability of IPD. The results of the feasibility assessment will inform the choice of ITC technique. The review also highlights the limitations of various ITC techniques, including the potential for bias, the need for sufficient overlap in covariate distributions, and the challenges of adjusting for large numbers of baseline differences. Overall, the review provides a comprehensive overview of ITC techniques and their strengths and limitations, with the goal of improving the quality of ITCs submitted to HTA agencies.A systematic literature review (SLR) was conducted to evaluate indirect treatment comparison (ITC) methods used in health technology assessment (HTA) to provide guidance on the most appropriate techniques for different scenarios. The review identified 73 articles reporting on seven ITC techniques: network meta-analysis (NMA), matching-adjusted indirect comparison (MAIC), network meta-regression (NMR), the Bucher method, simulated treatment comparison (STC), propensity score matching (PSM), and inverse probability of treatment weighting (IPTW). NMA was the most frequently described technique (79.5%), followed by MAIC (30.1%), NMR (24.7%), the Bucher method (23.3%), STC (21.9%), PSM (4.1%), and IPTW (4.1%). MAIC and STC were common in single-arm studies, while NMA and the Bucher method were suitable when individual patient-level data (IPD) were unavailable. ITC techniques aim to provide alternative evidence when direct comparisons are not possible. However, their acceptability remains low, and clearer international consensus is needed to improve their quality. ITC techniques are evolving rapidly, and more efficient methods may become available in the future. The review highlights that all ITC techniques are forms of adjusted indirect comparison. NMA and the Bucher method assume constant relative treatment effects, which may lead to biased estimates if cross-trial differences exist. MAIC and STC are population-adjusted methods that adjust for treatment effect modifiers (TEMs) to ensure comparability between populations. These methods are more complex and time-consuming than NMA but offer better accuracy in cases of unbalanced TEMs. PS-based techniques, such as PSM and PSW, use propensity scores to balance covariates and reduce selection bias in observational studies. However, they rely on the assumption that all relevant TEMs have been adjusted for and may be biased if unobserved variables are present. The review emphasizes the importance of selecting the appropriate ITC technique based on the feasibility of a connected network, the evidence of heterogeneity between and within studies, the number of relevant studies, and the availability of IPD. The results of the feasibility assessment will inform the choice of ITC technique. The review also highlights the limitations of various ITC techniques, including the potential for bias, the need for sufficient overlap in covariate distributions, and the challenges of adjusting for large numbers of baseline differences. Overall, the review provides a comprehensive overview of ITC techniques and their strengths and limitations, with the goal of improving the quality of ITCs submitted to HTA agencies.