The availability of vast amounts of data and improvements in machine learning algorithms have made artificial intelligence (AI) more powerful in recent years. These developments have enabled AI systems to comprehend and analyze complex patterns, generate precise forecasts, and improve their capabilities over time. This research explores the comparative analysis among three AI models (artificial neural network (ANN), least absolute shrinkage and selection operator (LASSO), and linear regression (LR)) trained on data from the explicit finite difference method (EFDM) to predict the heat and mass transfer (HMT) characteristics of a blood-based nanofluid over an inclined porous sheet (IPS). The coupled effects of Casson nanofluids, electromagnetic hydrodynamics (EMHD), thermal radiation, and activation energy with chemical reactions (AECR) are also considered in this mathematical modeling. The core objective is to analyze the intricate interplay between chemical reactions, activation energy, and phenomena such as HMT on IPS by comparing AI model results. The EFDM is used to convert the time-dependent governing Eqs. into a dimensionless form. To ensure accurate solution convergence, stability, and adherence to convergence criteria, they are established. The dimensionless parameters for which numerical results are shown graphically include temperature, velocity, and concentration distributions. The key finding is a significant decrease of around 65.6% in velocity on the flat surface. This translates to exceptional flow resistance, with an observed increase in friction of 18.47%. Furthermore, thermal performance shows an estimated 79.07% improvement, indicating enhanced heat-transfer efficiency. Additionally, for relevant physical parameters, the mass and heat transfer rates achieve R-squared values of 0.9999, signifying excellent agreement. The HMT says that the AI's LR algorithm achieves very high R-squared values of 0.9999 for each. These advancements hold promise for various applications, including enhanced academic research in relevant fields.