In various fields, the modeling of count data holds significant importance. The Poisson regression model is a commonly utilized tool for this purpose. However, this model assumes that the data has uniform dispersion, a condition often not met in real-world observations. The nature of overdispersion can vary depending on the specific context. When the overdispersion is primarily due to an excessive number of zero counts, the Zero-inflated Poisson regression model becomes a more suitable choice for modeling count data. The paper commences by offering a summary of the theoretical foundations of both Poisson regression and Zero-inflated Poisson regression. To evaluate their performance, use the Mean-Squared error (MSE) as a comparative metric. Next, apply these models to analyze the frequency of hospital emergency room visits by children between 10–18 years of age. The overdispersion of the visit count in our dataset is mostly caused by the excessive occurrence of zero counts. The findings demonstrate that the Zero-inflated Poisson regression model outperforms the standard Poisson regression model in terms of MSE.