The use of autonomous systems at wood processing sites of forest industries can significantly increase safety, productivity and efficiency by reducing the number of monotonous and dangerous tasks conducted by human labor utilizing heavy machinery. However, autonomous machine operation in mill yards is challenging because of the dynamic and complex working environment and partly unstructured processes. The inherent complexity of wood handling and storage tasks requires significant human expertise. Rapid advancements in sensor technologies and machine learning techniques, along with increases in available computational power have enabled progress in automated operation frameworks and algorithms development, which opens the door to the introduction of novel autonomous systems into this environment. With the aim of gaining a better understanding of current issues and facilitating optimal strategies for the deployment of high-level autonomous systems in mill yard environments, this study: (1) utilizes a systematic literature review to map current autonomous technologies and algorithms suitable for adoption by the forest industry in automation of vehicles working in mill yards; (2) summarizes and discusses the potential feasibility of the considered sensors, systems and adoption strategies, and considers implementation challenges for high-level autonomous machinery in mill yard environments; and (3) proposes a system framework that integrates multiple technologies to enable autonomous navigation and material handling in mill yards. The study is the first of its kind as a comprehensive study on autonomous vehicles and machinery in mill yard environments. Our novel framework aids in the identification of follow-up research areas and thus promotes the adoption and use of complex autonomous systems in industrial environments.