Human-in-the-loop machine learning is a methodological paradigm that deliberately incorporates human intelligence into the machine learning process, establishing an iterative cycle of model training and refinement through direct human interaction and feedback. This concept investigates the synergistic integration of human cognitive abilities and computational power to enhance learning performance, particularly in tasks where data is scarce, ambiguous, or requires domain expertise for accurate labeling or validation. Key characteristics include the iterative nature of the feedback loop, the active role of humans in providing annotations, corrections, or evaluations, and the system's ability to leverage this human input to improve model accuracy, robustness, or decision-making. Its significance lies in enabling the development of more accurate and reliable AI systems by efficiently utilizing human expertise to overcome limitations in data availability or purely automated learning processes.