This research introduces quantum adaptive machine learning (QAML) as an innovative solution to enhance the processing efficiency of machine learning in medical image classification, particularly for diagnosing brain tumors. QAML leverages quantum algorithms to address challenges associated with large and high-dimensional medical images, offering advantages such as accelerated processing rates, reduced model complexity, heightened accuracy, enhanced precision in handling intricate data relationships, and increased resistance to noise. The study details the implementation of QAML through a hybrid quantum-classical neural network, employing parameterized quantum circuits for image processing. Comparative experiments with traditional machine learning models demonstrate QAML's faster convergence and comparable accuracy. The research also explores the impact of adaptive optimization strategies on QAML performance, indicating promising results. Overall, QAML, with its quantum convolutional and pooling layers, emerges as a promising and efficient solution for medical image classification, marking significant progress in the integration of quantum-enhanced machine learning in healthcare applications.This research introduces quantum adaptive machine learning (QAML) as an innovative solution to enhance the processing efficiency of machine learning in medical image classification, particularly for diagnosing brain tumors. QAML leverages quantum algorithms to address challenges associated with large and high-dimensional medical images, offering advantages such as accelerated processing rates, reduced model complexity, heightened accuracy, enhanced precision in handling intricate data relationships, and increased resistance to noise. The study details the implementation of QAML through a hybrid quantum-classical neural network, employing parameterized quantum circuits for image processing. Comparative experiments with traditional machine learning models demonstrate QAML's faster convergence and comparable accuracy. The research also explores the impact of adaptive optimization strategies on QAML performance, indicating promising results. Overall, QAML, with its quantum convolutional and pooling layers, emerges as a promising and efficient solution for medical image classification, marking significant progress in the integration of quantum-enhanced machine learning in healthcare applications.