Zero-Inflated Models are statistical tools used to analyze count data that have an excess of zero values. These models combine two processes: one that generates only zeros and another that produces counts, allowing for a better fit when traditional models, like the Poisson or Negative Binomial, struggle with overdispersion or an abundance of zeros. These models are particularly useful in fields such as ecology, healthcare, and economics, where data often includes many instances of zero counts. By accounting for the two different processes, Zero-Inflated Models provide more accurate predictions and insights into the underlying data structure.