Cantaloupe has been recognized as a common vehicle for foodborne infections among fresh produce commodities. A severe multistate outbreak of Listeria monocytogenes associated with the consumption of whole cantaloupe resulted in 33 deaths in 2011. Quantitative microbial risk assessment (QMRA) modeling in food safety risk analysis has been acknowledged as an efficient tool to estimate and provide knowledge needed to manage potential public health risks associated with foodborne pathogens. The objectives of this study were to (i) conduct a microbiological survey of pre-harvest cantaloupes from farms in mid-Atlantic region, and (ii) develop a "farm to table" QMRA model for L. monocytogenes in cantaloupe. The results of the regional microbiological survey indicated a 5.3% (2/38) prevalence of generic Escherichia coli and negative for L. monocytogenes and Salmonella on cantaloupe during harvest season. A QMRA model was developed based on a thorough review of data from scientific publications and communications with fresh-cut processing industry. The model was simulated with Monte Carlo technique for 100,000 iterations in @Risk. The model estimated the public health risks associated with the consumption of both fresh-cut and whole cantaloupes in the U.S. The model demonstrated the risk associated with the consumption of a serving of fresh-cut cantaloupe is around 10 times higher than that for whole cantaloupe. Using the baseline model, the estimated median number of listeriosis cases per year associated with the consumption of fresh-cut cantaloupe among susceptible subpopulation and general healthy population are 0.0368 and 0.00134, respectively. Sensitivity analysis suggested temperature control during retail (correlation coefficient: 0.69) and home storage (correlation coefficient: 0.48) are two critical factors in mitigating the risk for fresh-cut cantaloupe consumption while home storage temperature (correlation coefficient: 0.79) after cutting is the most important factor for whole cantaloupe consumption. The QMRA model provided critical information for risk management and identified the critical data gaps including initial contamination and prevalence for future risk assessments of melon.