Recent technological advances in the underwater sensing instrumentation provide currently active multibeam echosounders that can acquire backscatter observations from multiple spectral frequencies. In this paper, the main objective was to design, develop and validate an efficient and robust multispectral, multibeam data processing framework including advanced machine learning tools for seabed classification. In order to do so, we have integrated different machine learning tools like support vector machines and random forests towards the classification of seabed classes. We have performed extensive experiments with different splitting ratios, regarding training and testing sets, in order to assess possible overfitting. The entire pipeline has been implemented in a scalable containerized manner in order to be deployed in cloud infrastructures and more specifically at the European Open Science Cloud. Experimental results, the performed qualitative and quantitative evaluation along with the comparison with the state of the art indicated the quite promising potential of our approach.