This paper aims to find an automatic solution for the modulation’s classification of different types of radio signals by relying on Artificial Intelligence. This project is part of a long process of Communications Intelligence looking for an automatic solution to demodulate, decode and decipher communication signals. Our work therefore consisted in the choice of the database needed for supervised deep learning, the evaluation of existing techniques on raw communication signals, and the proposal of a solution based on deep learning networks allowing to classify the types of modulation with an optimal ratio (computation time / accuracy). We first carried out a research work on the existing models of automatic classification in order to use them as a reference. We consequently proposed an ensemble learning approach based on tuned ResNet and Transformer Neural Network that is efficient at extracting multi- scale features from the raw I/Q sequence data and also considers the challenge of predicting in low Signal Noise Ratio (SNR) conditions. In the end, we delivered an architecture that is easy to handle and apply to communication signals. This solution has an optimal and robust architecture that automatically determines the type of modulation with an accuracy up to 95%