The "COVID-Detection-Gompertz-Function-Ensemble" project involves predicting COVID-19 case trends using the Gompertz function and ensemble learning methods. First, the Gompertz function models the growth curve of COVID-19 cases, capturing the pandemic's progression over time. Next, ensemble techniques combine multiple predictive models, such as Random Forest and Gradient Boosting, to enhance forecasting accuracy and robustness. This approach integrates historical case data, estimates model parameters, and uses advanced algorithms to provide reliable predictions and insights into future COVID-19 trends.