This study conducted an economic simulation to estimate the global economic impacts of 12 dairy cattle diseases and health conditions across 183 milk-producing countries. The diseases included mastitis (subclinical and clinical), lameness, paratuberculosis (Johnes disease), displaced abomasum, dystocia, metritis, milk fever, ovarian cysts, retained placenta, and ketosis (subclinical and clinical). Disease impacts on milk yield, fertility, and culling were collected from literature, standardized, meta-analyzed, and adjusted for comorbidities to prevent overestimation. These adjusted impacts were combined with country-level incidence or prevalence estimates, herd characteristics, and price estimates using Monte Carlo simulations to value economic losses. The total annual global losses were estimated at US$65 billion, with subclinical ketosis, clinical mastitis, and subclinical mastitis being the costliest diseases, resulting in mean annual global losses of approximately US$15 billion, US$13 billion, and US$9 billion, respectively. The study also found that without comorbidity adjustment, mean aggregate global losses would have been overestimated by 45%. While India, the United States, and China had the highest annual losses, the relative economic burden of these diseases varied significantly across countries. The study highlights the importance of considering comorbidities in economic analyses to avoid overestimating the economic impact of dairy cattle diseases.This study conducted an economic simulation to estimate the global economic impacts of 12 dairy cattle diseases and health conditions across 183 milk-producing countries. The diseases included mastitis (subclinical and clinical), lameness, paratuberculosis (Johnes disease), displaced abomasum, dystocia, metritis, milk fever, ovarian cysts, retained placenta, and ketosis (subclinical and clinical). Disease impacts on milk yield, fertility, and culling were collected from literature, standardized, meta-analyzed, and adjusted for comorbidities to prevent overestimation. These adjusted impacts were combined with country-level incidence or prevalence estimates, herd characteristics, and price estimates using Monte Carlo simulations to value economic losses. The total annual global losses were estimated at US$65 billion, with subclinical ketosis, clinical mastitis, and subclinical mastitis being the costliest diseases, resulting in mean annual global losses of approximately US$15 billion, US$13 billion, and US$9 billion, respectively. The study also found that without comorbidity adjustment, mean aggregate global losses would have been overestimated by 45%. While India, the United States, and China had the highest annual losses, the relative economic burden of these diseases varied significantly across countries. The study highlights the importance of considering comorbidities in economic analyses to avoid overestimating the economic impact of dairy cattle diseases.