Abstract: Autonomous navigation of agricultural machinery has been the fundamental and core pillar in the advancement of precision agriculture. The production efficiency and product quality can be enhanced to simultaneously reduce the resource consumption and environmental footprint. This review aims to systematically examine the research progress in the autonomous navigation technology for agricultural machinery. A particular emphasis was placed on the current state-of-the-art and emerging trends within two critical technological domains: navigation positioning and navigation control. Significant efforts were also made on the navigation positioning. Specifically the high-precision global navigation satellite system (GNSS) (real-time kinematic (RTK) and precise point positioning (PPP)) was delivered the centimeter-level absolution of the positioning accuracy. Concurrently, machine vision approaches provided robust solutions for the centimeter-level relative positioning. Both conventional algorithms and increasingly powerful deep learning techniques were balanced for the feature extraction and scene understanding, along with light detection and ranging (LiDAR) systems. Some limitations were also proposed after the comparison. Single-sensor systems were recognized in the complex, dynamic agricultural environments. The dominant and most effective paradigm emerged as the multi-sensor fusion strategies integrating GNSS, inertial navigation systems (INS), vision, and LiDAR data. This convergence of data streams was essential to enhance system resilience, reliability, and adaptability across diverse and challenging field conditions. In navigation control technologies, solutions were tailored to different machinery classes. Hydraulic steering systems remained the preferred choice for heavy-duty agricultural equipment, due to their high force. While the electric steering systems offered distinct advantages, in terms of precision, responsiveness, and integration ease, increasingly suitable for medium and small-sized machinery. Substantial progress was achieved in the path-tracking algorithms. Some techniques, such as Model Predictive Control (MPC), were used to predict the future states for the optimal control actions. Adaptive Pure Pursuit methods dynamically adjust look-ahead distances for smoother tracking. Intelligent optimization algorithms further enhanced the accuracy and the robustness of path following. Advanced control strategies demonstrated the superior performance, particularly under demanding operating scenarios like steep slopes, uneven terrain, and conditions prone to wheel slip or side-slip. Technological evolution was found in the practical implementation and commercialization. International manufacturers of agricultural machinery, including John Deere and CLAAS, successfully transitioned the high-precision autonomous navigation solutions into the large-scale, commercially viable products widely adopted in modern farming. National enterprises, such as Huace Navigation and Shanghai Lianshi, also deployed navigation systems. BeiDou navigation satellite system (BDS) was also utilized under various agricultural scenarios. Looking ahead, future research should strategically focus on several key directions: The intelligent context-aware fusion for the multi-modal sensor data streams; navigation algorithms optimization specifically for highly specialized agricultural tasks and environmental conditions; and the deeper, more seamless integration of autonomous navigation with the precision agricultural implements and operations. These avenues can be instrumental for agricultural machinery autonomous navigation towards the higher levels of intelligence, broader applicability, and greater practical utility. This trajectory can provide a strong reference to accelerate the modernization and sustainable transformation of global agriculture.