Background: Modelling studies estimate the effects of an intervention on valued health consequences and costs using mathematical modelling. There is a renewed interest in these studies as an approach to inform decision-makers, particularly when an intervention’s comparative effectiveness or cost-effectiveness is unclear due to a lack of evidence addressing the question. Methods: We conducted an overview of a systematic review (SR) of decision analytical models, including decision tree models, state transition models, discrete event simulation, and dynamic transmission models, using the databases of Medline, EMBASE, and the Cochrane Library. We assessed the reporting quality using the AMSTAR-2 tool for each SR. We described the characteristics, quality assessment, methods of synthesizing the results, and certainty of evidence applied in each SR. Results: Preliminarily, we included 17 SRs published between 2018 and 2021, specifically for pharmacological, prevention, digital health, screening, and computer navigation interventions. Most of the SRs have limitations in quality reporting. Approximately 88% didn't apply a risk of bias tool to evaluate the modelling studies. None of them evaluated the certainty of the evidence. Approximately 94% conducted a narrative synthesis of the results; however, only one SR performed a quantitative synthesis for the ICER measure and its confidence interval. Conclusion: There is a lack of quality reporting and methods in systematic reviews of decision analytical modelling. New approaches are necessary to guide the performance of systematic reviews in this type of study design.