Abstract
This study focuses on the creation of a system for machine learning model designed, monitor, detect, and classify the underlying reasons behind infant crying. Leveraging acoustic features, the model will differentiate between various causes, including hunger, discomfort, pain, and more, with the goal of providing real- time insights for caregivers and healthcare professionals. This innovative technology aims to enhance infant care by facilitating early detection and appropriate responses to the infant's needs, ultimately contributing to the well-being and overall health of newborns and reducing caregiver stress. Keywords: Machine learning, Acoustic analysis, Infant cry, MFCC, KNN, python_speech_features, Django, MySQL.
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