CoCrNi-TiCx exhibits excellent high-temperature oxidation resistance and wear resistance, making it an ideal material for engine valve seats. This study utilizes laser cladding technology to integrate the composite material into the cylinder head. Here, we propose a machine learning (ML) approach for manufacturing design, where the correlations between laser cladding process parameters and the geometrical characteristics of CoCrNi medium entropy alloy coatings were established by a neural network. This model achieves a remarkable high accuracy level with coefficients of determination of up to 0.996. The optimal parameter-characteristic-space was successfully applied to fabricate CoCrNi-TiCx (x = 0, 1.0, 3.0, 5.0, 7.0 wt%) composite coatings. With the addition of micro-sized TiC particles from 0 to 5.0 wt%, the average friction coefficient at room temperature gradually decreased, and the effective wear mechanisms could be characterized by adhesive wear, abrasive wear, and less pronounced oxidative wear. At a testing temperature of 700 °C, the number of oxide layers covering surface of the wear track markedly increased. Additionally, the content of TiO₂ in these oxide layers gradually rose and they affected the stress adjustment through deformation. This behavior effectively enhanced the protective effect of the oxide layers and improved the wear properties.