Automated machine learning, known as AutoML, is a process that utilizes automated techniques and algorithms to develop machine learning models. This encompasses feature selection and preprocessing, algorithm selection, hyperparameter optimization, and model performance evaluation. The goal of AutoML is to simplify and expedite the model building process, enabling individuals with less data science experience to construct effective models without the need to manually navigate all the stages. In the context of an economic study on Ecuador, it is proposed to employ AutoML to analyze the country’s economic data and forecast the Gross Domestic Product (GDP) growth. Various factors that have historically impacted Ecuador’s GDP are taken into consideration, such as reliance on the export of a single raw material, political and economic instability, fiscal deficits, fluctuations in oil prices, financial crises, and the COVID-19 pandemic. It is suggested to utilize a Vector Autoregressive (VAR) model within the machine learning process. VAR is a statistical model that allows us to comprehend how various variables co-vary over time. Additionally, different regression analysis algorithms are mentioned, such as decision trees, k-nearest neighbors, random forest, support vector machines, and artificial neural networks, which can be employed in the analysis of economic data.