This study used a high pressure optical cell (HPOC) combined with a heating/cooling stage, a pressure device, and a laser Raman spectrometer to investigate the volume expansion (VE) of CO₂ + tetradecane and CO₂ + hexadecane mixtures under geological storage conditions. Abrupt changes in VE were observed near the CO₂ critical point (CP), which is assumed to be ubiquitous in CO₂ + long-chain alkane mixtures with >= 14 carbon alkanes. This is due to the critical anomaly of the thermodynamic behavior of the mixture near the CP of CO₂, which inhibits the VE of CO₂ + long-chain alkanes at lower temperatures. In addition, four machine learning (ML) algorithms, including backpropagation neural network (BPNN), K-nearest neighbors (KNN), random forest (RF), and support vector regression (SVR), were applied to predict the volume expansion factor (VEF). The BPNN model provided much higher robustness and accuracy with correlation coefficient (R-2) >0.98 and mean square error (MSE) <0.03, which indicates a strong agreement between the predicted result and matching experimental data. We concluded that a predictive framework built using ML could provide rapid predictions of the volume change curve or curved surface of CO₂ + organic mixtures under various temperature and pressure conditions.