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Asphaltenes create severe problems in light oils (LO) and heavy crude oils (HO) production, therefore understanding the proper asphaltenes adsorption is a demanding topic to circumvent asphaltene deposition and reconfigure asphaltene viscoelastic networks. The aim of this work is to develop several artificial intelligence (AI) agents that accurate predict the asphaltene adsorption produced by different types of nanoparticles. More than 200 experimental data points were used including different types of crude oils (light oils, heavy oils, and extra-heavy oils) combined with different types of nanoparticles including silica, alumina, and cerium, among others. This work not only presents for the first time a general AI agent that predicts the adsorption isotherms of asphaltene including different types of nanoparticles, but also deploys specialized AI agents that predict adsorption isotherms of asphaltene exclusively for silica, alumina, and cerium nanoparticles.