The present study explores the behavior of Casson fluid over a stretching membrane with a porous medium, focusing on the effects of chemical reactions and the absorption of heat during melting. The fluid is assumed to be a ternary hybrid nanofluid (THF), composed of \(T{i}_{4}A{l}_{6}V\) , AA7072 and AA7075 nano-particles suspended in the sodium alginate (NaAlg) as the base fluid, whose analysis is conducted through artificial intelligence (AI)-based machine learning mechanisms. The Darcy-Forchheimer effect, which is caused by the porous medium, is represented by the modified momentum equation. Additionally, the melting process significantly influences heat transfer. A simplified mathematical model is developed to examine the heat transfer properties in a state of local thermal disparity, which allows distinct thermal gradients within both the solid and liquid states. The nonlinear partial differential equations (PDEs) are transformed, with the help of appropriate transformations, into ordinary differential equations (ODEs), and a synthetic dataset is obtained using Python, passed through an Artificial Neural Network (ANN). The influence of key variables on velocity and temperature profiles is illustrated and analyzed using solution graphs and hyperparametric plots. The findings indicate that an increase in the melting parameter elevates the temperature and the velocity distributions. Moreover, the rise of the interphase thermal exchange parameter enhances the rate of heat transfer with the solid phase.
