Impellers within turbomachinery face critical challenges related to material wear, maintenance costs, performance, and efficiency. The optimization of impellers has been extensively studied to address these issues across components such as turbines, pumps, compressors, fans, and mixers. The objective of this comprehensive review is to explore current state-of-the-art techniques for resolving these problems in the field of additive manufacturing (AM) and optimization methods. We conducted an exhaustive search of scientific articles in major databases, meticulously filtering relevant information from high-quartile sources. The study reveals various AM techniques applied to impellers and adjacent elements, along with diverse materials used in functional system components. Additionally, we describe the positive effects of optimization methods, including Multi-objective Optimization (MO), Artificial Neural Networks (ANN), Response Surface Method (RSM), and Genetic Algorithm (GA), on turbomachinery part design. Recent trends indicate increased variability in optimization approaches, often combining multiple techniques or optimization models for optimal results. Regarding AM, evidence suggests that Fused Deposition Modeling (FDM) and powder bed fusion technology are the most widely used methods in this field. The materials used in AM processes are very varied and depend on their applications and can be metals (Ti-6Al-4V, Inconel 718, AISI316L, 17-4 PH), polymers (ABS, nylon, PLA, PU), resins and ceramics.