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Simple Summary: Thermal ablation is an established treatment for primary and secondary liver tumors. As ablation treatment planning is a fast-emerging field, accurate and patient-specific ablation zone simulation may contribute to higher efficacy of thermal ablation. Computational modeling could facilitate these simulations. This systematic review aims to identify, evaluate, and summarize the findings of the literature on existing computational models for thermal liver ablation planning and compare their accuracy. The literature shows a wide variety of computational modeling and validation methods. Additional research, with a focus on shape-based outcome metrics, is warranted to determine which model demonstrates superior accuracy and suitability for clinical practice. More insight into parameter personalization is required to enable patient-specific ablation planning. Purpose: This systematic review aims to identify, evaluate, and summarize the findings of the literature on existing computational models for radiofrequency and microwave thermal liver ablation planning and compare their accuracy. Methods: A systematic literature search was performed in the MEDLINE and Web of Science databases. Characteristics of the computational model and validation method of the included articles were retrieved. Results: The literature search identified 780 articles, of which 35 were included. A total of 19 articles focused on simulating radiofrequency ablation (RFA) zones, and 16 focused on microwave ablation (MWA) zones. Out of the 16 articles simulating MWA, only 2 used in vivo experiments to validate their simulations. Out of the 19 articles simulating RFA, 10 articles used in vivo validation. Dice similarity coefficients describing the overlap between in vivo experiments and simulated RFA zones varied between 0.418 and 0.728, with mean surface deviations varying between 1.1 mm and 8.67 mm. Conclusion: Computational models to simulate ablation zones of MWA and RFA show considerable heterogeneity in model type and validation methods. It is currently unknown which model is most accurate and best suitable for use in clinical practice. [ABSTRACT FROM AUTHOR]