Tool condition monitoring (TCM) is a key technology for ensuring machining quality, improving production efficiency, and extending the service life of computer numerical control (CNC) machine tools. As a core component of the machining system, the cutting tool is subject to failure modes such as wear and chipping, which directly affect machining accuracy and system reliability. Influenced by variations in cutting parameters, fluctuations in operating conditions, and environmental noise, the tool wear process exhibits continuous, irreversible, and uncertain characteristics, resulting in evident limitations of single-sensor signals in terms of information completeness and anti-interference capability. By integrating the complementary advantages of multi-sensor signals such as cutting force, vibration, acoustic emission, current, and power, multi-source signal fusion provides an effective approach for achieving highly accurate and robust online tool condition monitoring. Focusing on the application of multi-source signal fusion in TCM, this work presents a systematic review of relevant theoretical frameworks and research progress. Common sensor types, including cutting force, vibration, and acoustic emission sensors, as well as their integration methods, are analyzed, and the performance differences among various sensors in terms of signal acquisition accuracy, anti-interference capability, and response characteristics are compared. Subsequently, data-level, feature-level, and decision-level fusion strategies are discussed, including filtering algorithms, machine learning models, and uncertainty reasoning methods. Typical applications such as tool breakage detection, wear monitoring, and remaining useful life prediction are reviewed to reveal the advantages of multi-source signal fusion in improving monitoring accuracy and system reliability. Finally, current challenges and future directions are summarized, providing theoretical and practical references for tool lifecycle monitoring.