<p>Biomaterials are substances created specifically to work with biological systems. While some biomaterials are bioinert and non-degradable (e.g., metal-/alloy-/ceramic-/polymer-based biomaterials that could be used as hip replacements), other biomaterials are degradable and can be resorbed and replaced by healthy tissue. Many biomaterials such as metals and metallic alloys, ceramics, synthetic polymers, and natural materials have been conceived and developed as viable alternatives, and they have been effectively used in a variety of biomedical sectors. Recently, numerous Nature-Inspired Optimization Algorithms (NIOAs) have been developed, drawing inspiration from various natural processes. Such Algorithms effectually solve complex problems by balancing exploration and exploitation, and adapting to diverse, large-scale, and dynamic optimization challenges. NIOAs are computational optimization frameworks derived from biological, ecological, or physical natural processes. They employ adaptive, stochastic search mechanisms to navigate high-dimensional and nonlinear solution spaces. In biomaterials science, they enable systematic optimization of material composition, structural morphology, and functional performance for enhanced biocompatibility and engineered functionality. In this review work, the author examines the use of Nature-Inspired Optimization Algorithms (NIOAs) in the modelling and synthesis of sustainable biomaterials. Current work analytically scrutinizes the role of nature-inspired optimization algorithms in the engineering of sustainable biomaterials, highlighting current trends, applications, challenges, and future research opportunities.</p>