Low fracture toughness is the Achilles heel of many polymers for practical application, particularly for long-term and special applications such as medical implants and critical part design. The toughening of polymers is a complicated process, and the toughening mechanisms in many composite systems have not yet been fully understood. Machine learning (ML) has emerged as a promising tool for modeling various complex systems in the field of materials science. In this respect, ML appears to be an effective, affordable, and accurate technique in the modeling of the toughening process. This review article provides an overview of the recent ML-based approach for analyzing toughening effects in rigid particulate-filled polymer composites. The results summarized here would contribute to further possibilities for researchers to explore emerging trends for designing toughing and high-performance polymers.