Proteins are essential fundamental substance for life processes, performing a variety of key roles in organisms, such as constructing cell structures, participating in metabolism and energy transformation, regulating physiological functions, providing immune protection, and transmitting signals. The diverse functions of proteins are achieved through their unique amino acid sequences and corresponding three-dimensional structures. Protein engineering involves altering or designing protein sequences and structures to achieve specific functions, and these efforts enhance our knowledge of proteins and offer powerful tools and technical support for research in biomedicine, biomaterials, bioengineering, and related fields. In recent years, with the advancements in algorithms, the accumulation of big data, and improvements in hardware computational power, artificial intelligence technology has rapidly developed and gradually been applied in the field of protein engineering, leading to the emergence of intelligent protein engineering. By utilizing biological big data such as genomics, proteomics, protein structure databases, and establishing various advanced deep learning models based on the data, intelligent protein engineering can achieve efficient, precise, and predictable protein design and modification. This article primarily focuses on four aspects of intelligent protein engineering, including structure design, backbone-free sequence design, backbone-based sequence design, and other auxiliary design approaches, summarizing the latest progress in the artificial intelligence technologies employed in these fields, and compiling the practical results achieved in recent years using intelligent protein engineering technology. As an emerging technology and method, intelligent protein engineering demonstrates significant potential and prospects, bringing substantial impacts on future scientific research and technological innovation, and providing new solutions and tools for addressing global challenges.