In the realm of regenerative medicine, tissue engineering has gained promise as a means of creating viable replacements for diseased or damaged tissues. The optimization of tissue engineering constructions has benefited greatly in recent years from the convergence of machine learning (ML) and artificial intelligence (AI) techniques. In order to improve tissue engineering build performance and design, this work investigates the synergistic integration of ML and AI techniques with a particular focus on feature engineering. The study starts out by outlining the difficulties in obtaining the best possible build functioning and reviewing the state-of-the-art in tissue engineering. It then delves into the application of ML algorithms, such as regression, classification, and clustering, to analyze diverse datasets related to biomaterial properties, cellular behavior, and environmental factors. Simultaneously, deep learning and neural networks are used as AI tools to uncover complex patterns and relationships from the data. The use of sophisticated feature engineering techniques is a key component of this research since it helps to improve model interpretability and retrieve pertinent data. Techniques such as dimensionality reduction, feature selection, and representation learning are explored to refine input features, enhance model accuracy, and uncover meaningful insights. The paper discusses case studies where the combined ML and AI approaches, integrated with feature engineering, have successfully optimized the fabrication of tissue engineering constructs. Examples include the identification of optimal biomaterial compositions, prediction of cell behavior in response to varying stimuli, and real-time monitoring of construct performance. The results demonstrate the ability of these integrated approaches to accelerate the design process and improve the overall efficacy of tissue engineering applications.