In this paper, we present a mathematical model to analyze the dynamics of leptospirosis and COVID-19 co-infection. The model used actual data, and estimation of the parameters via the MLE method is performed, which includes the rates of disease transmission, progression of the disease, disease-related death, and recovery rates for each disease and their co-infection. Key parameters: $$\beta _1$$
β
1
, $$\beta _2$$
β
2
, $$\phi _1$$
ϕ
1
, $$\phi _2$$
ϕ
2
, and $$\mu _c$$
μ
c
are used to characterize the dynamics of the co-infection burden of leptospirosis and COVID-19. The results reveal a notable contrast in transmission dynamics and clinical outcomes between the two diseases, with leptospirosis demonstrating a higher transmission rate and increased morbidity. Co-infections showed more severe clinical outcomes, with higher mortality and delayed recovery than single infections. These findings highlight the importance of targeted public health strategies for managing co-infected populations.