Thermal flooding is one of the most successful and widely used processes for heavy oil recovery. The memory-based fluid flow model is effective in characterizing reservoir heat transport mechanism, temperature profile, and predicting the performance of thermal recovery. Temperature has a substantial effect on the thermodynamic properties such as thermal conductivity of the formation. In addition, the influence of temperature on reservoir rock and fluid properties plays an important role in accurately predicting reservoir temperature distribution, oil displacement, and steam oil ratio. This paper presents a critical review and analyses to provide inclusive information on the state-of-the-art memory-based fluid flow modeling during the thermal displacement process. The review highlights the assumptions and limitations of the current models in the areas of thermal conductivity, temperature distribution, oil displacement, and steam oil ratio during the thermal flooding process in porous media. This paper also serves to provide an insight into future research opportunities to fill the knowledge gaps in the subject area by applying the memory concept and further improvement of the current and classical models for heavy oil recovery.