Real-time gas classification is an essential issue and challenge in applications such as food and beverage quality control, accident prevention in industrial environments, for instance. In recent years, the Deep Learning (DL) models have shown great potential to classify and forecast data in diverse problems, even in the electronic nose (E-Nose) field. In this work, we used a Support Vector Machines (SVM) algorithm and three different DL models to validate the rapid detection approach (based on processing an early portion of raw signals and a rising window protocol) over different measurement conditions. We performed a set of trials with five different E-Nose databases that include fifteen datasets. Based on the results, we concluded that the proposed approach has a high potential, and it can be suitable to be used for E-nose technologies, reducing the necessary time for making forecasts and accelerating the response time. Because in most cases, it achieved reliable estimates using only the first 30% or fewer of measurement data (counted after the gas injection starts.) The findings suggest that the rapid detection approach generates reliable forecasting models using different classification methods. Still, SVM seems to obtain the best accuracy, right window size, and better training time.