Abstract: Rock collapse disasters, characterized by their broad spatial distribution, recurrent occurrence, and abrupt unpredictability, are considered one of the most formidable geological hazards that can be monitored and predicted among the three major geological disasters. To address the limitations of current macroscopic displacement-based monitoring methods in meeting early warning requirements for rock collapse, we propose a novel slope dynamics theory based on Newton’s second law and develop micro-core intelligent monitoring and early warning technology, thereby establishing an innovative “technology-management integration” framework for situational awareness and disaster prevention/mitigation of sudden geotechnical hazards. This study systematically analyzes and discusses theories of rock mass instability dynamics, monitoring and early warning technologies, critical issues, and application prospects using case studies from four key perspectives: theoretical framework, technological innovation, methodological challenges, and practical implementation. First, transcending the traditional limit equilibrium theory, a slope dynamics theory based on Newton’s second law is proposed. Using a pendulum proton model generalized from dangerous rock masses, a dynamic stability evaluation method is established to characterize the degree of detachment, reflecting the nonlinear failure characteristics of dangerous rocks. Second, a “four-in-one” monitoring index system integrating static, dynamic, kinematic, and environmental parameters was developed. Building upon cloud-edge integration technologies for intelligent dynamic sensing equipment, a disaster prevention concept combining technical monitoring and engineering management is formulated for dangerous rock collapses. Finally, future research directions for dynamic monitoring and early warning systems are proposed. The proposed slope dynamics theory is applied to reservoir rock slopes and mine-side slope rock masses to provide references for scientific prevention and control research on sudden brittle disasters. The key innovations include the following: (1) the development of a slope dynamics theory that transcends traditional limit equilibrium analysis. (2) A pendulum proton model generalized from unstable rock masses enables dynamic stability assessment by quantifying the detachment severity through nonlinear failure characteristics. (3) Creation of a four-dimensional monitoring index system integrating static, dynamic, kinematic, and environmental parameters. By leveraging cloud-edge collaborative intelligent sensing, this framework combines technical monitoring with engineering governance to prevent rock collapses. (4) Case validations of the Houziyan open-top rock slope and surrounding rock mass in the Hainan metal mine demonstrated the operational efficacy of the proposed real-time multi-model early warning system. The dynamics theory resolves stability evaluation challenges across rock collapse evolutionary phases, whereas four-dimensional metrics enable data-driven safety alerts. To achieve early warning of brittle rock collapse disasters and overcome the critical limitation that conventional displacement-based warning technologies can only provide last-minute alerts (typically on a minute-to-second timescale), developing advanced early warning mechanisms with intelligent sensing systems is imperative for future disaster prevention. This study comprehensively reviewed emerging destabilization dynamics theories, intelligent sensing technologies, and novel technology-management integration frameworks from a multidisciplinary perspective. Considering two representative case studies, we systematically examined the practical implementation of dynamic monitoring and early warning systems in geological disaster prevention. Future efforts must prioritize theoretical robustness, sensor innovation, and artificial intelligence (AI) integration to enable proactive disaster prevention in infrastructure, mining, and reservoir projects.