Polycystic Ovary Syndrome (PCOS) is a disease of endocrine hormone regulation which plagues millions of reproductively active women with ovarian cysts and hormonal imbalance. The manuscript is based on a complex web application, which integrates deep-learning-based ovarian cyst identification and menstrual cycle monitoring, thus enabling self-diagnosis and self-management. The Convolutional Neural Network (CNN) was built and trained using ultrasound images consisting of control, cystic, and severe cyst types, therefore, making an accurate determination of the ovarian conditions. The model was implemented on a Flask application that allowed users to analyze images in real-time and received diagnostic feedback. In addition, the device has an interactive calendar that is used to document and track a monthly menstrual cycle. Using artificial intelligence and one-down-clicks interface, the platform hopes to transform the health management of women by detecting it properly, raising awareness, and proactively tracking it.