To estimate the cost-effectiveness of available diagnosis alternatives for Mucosal Leishmaniasis (ML) in Colombian suspected patients. A simulation model of the disease’s natural history was built with a decision tree and Markov models. The model´s parameters were identified by systematic review and validated by expert consensus. A bottom-up cost analysis to estimate the costs of diagnostic strategies and treatment per case was performed by reviewing 48 clinical records of patients diagnosed with ML. The diagnostic strategies compared were as follows: 1) no diagnosis; 2) parasite culture, biopsy, indirect immunofluorescence assay (IFA), and Montenegro skin test (MST) combined ; 3) parasite culture, biopsy, and IFA combined; 4) PCR-miniexon; and 5) PCR-kDNA. Three scenarios were modeled in patients with ML clinical suspicion, according to ML prevalence scenarios: high, medium and low. Adjusted sensitivity and specificity parameters of a combination of diagnostic tests were estimated with a discrete event simulation (DES) model. For each alternative, the costs and health outcomes were estimated. The time horizon was life expectancy, considering the average age at diagnosis of 31 years. Incremental cost-effectiveness ratios (ICERs) were calculated per Disability Life Year (DALY) avoided, and deterministic and probabilistic sensitivity analyses were performed. A threshold of willingness to pay (WTP) of three-time gross domestic product per capita (GDPpc) (US$ 15,795) and a discount rate of 3% was considered. The analysis perspective was the third payer (Health System). All costs were reported in American dollars as of 2015. PCR- kDNA was the cost-effective alternative in clinical suspicion levels: low, medium and high with ICERs of US$ 7,909.39, US$ 5,559.33 and US$ 4,458.92 per DALY avoided, respectively. ML diagnostic tests based on PCR are cost-effective strategies, regardless of the level of clinical suspicion. PCR-kDNA was the most cost-effective strategy in the competitive scenario with the parameters included in the present model.