<p>Modern manufacturing environments require dynamic, data-driven facility layouts that can adapt to changing demands for efficiency, resilience, sustainability, and flexibility. Advancements in Industry 4.0 technologies have enabled transformative methods for optimizing layout design. Unlike previous reviews that emphasize traditional algorithms or single technologies, this paper presents a structured literature review that synthesizes recent applications of machine learning, digital twin technologies, and augmented and virtual reality in facility layout planning and broader manufacturing layout planning contexts. Application domains, methodological approaches, dataset and performance characteristics, and integration challenges are the four dimensions examined in this review. The results show that digital twin frameworks facilitate real-time simulation, monitoring, and continuous layout evaluation; machine learning enables adaptive and sequential optimization through predictive modeling, reinforcement learning, and surrogate-assisted techniques; augmented reality supports in-situ spatial validation within physical environments while virtual reality enables immersive design exploration in synthetic ones, together improving ergonomics evaluation and collaborative decision-making. Although each technology provides significant benefits, fully integrated machine learning, digital twin, and augmented or virtual reality systems remain limited and face challenges related to heterogeneous data sources, scalability, and interoperability. This review contributes a structured map of current approaches and presents a forward-looking roadmap for intelligent, interoperable, and human-centered manufacturing facility layout planning in the Industry 4.0 era by synthesizing trends, research gaps, and emerging technological synergies.</p>