Systematic reviews and meta-analyses play a crucial role in clinical research by providing a means of evaluating the effectiveness of interventions in situations of uncertainty. Pairwise meta-analysis, which is the most widely used method, compares active interventions to placebos or other treatments. However, this approach has limitations in its ability to assess multiple interventions simultaneously, making it less suitable for comprehensive decision-making. This is where Network Meta-analysis (NMA) comes in, which extends pairwise meta-analysis to allow for the assessment of more than two interventions within a single analysis, even when direct head-to-head comparisons are not available. NMA shares similarities with pairwise meta-analysis, including systematic literature searches, bias assessment, data extraction, and statistical pooling. Two critical assumptions underlie NMA: transitivity and consistency. NMA can be performed using frequentist or Bayesian approaches, with both fixed and random effects models. Recent developments such as population adjustment methods and Component NMA have enhanced its utility. The significant advantage of NMA is its ability to generate treatment rankings based on the probability of each treatment being the most effective. Web-based applications such as MetaInsight and NMA Studio simplify the NMA process, making it more accessible without coding skills. NMA is essential in evidence-based decision-making, providing comprehensive comparisons of multiple interventions, overcoming the limitations of pairwise meta-analysis. While challenges persist, transparency is maintained, and decision-making bodies recognize NMA’s value. NMA is a powerful tool that defines the future of healthcare decision-making.