This study proposes a mobile-based remote dog care application that offers features such as smart cage management, obesity level checker, behavioral health issues identification, and health assistance. The application uses ICT technology to combine smart devices, allowing pet owners to monitor and manage multiple activities of their pet, including temperature, lightness, darkness, and humidity indicators. Furthermore the project aims to monitor dog behavior at home using a mobile app and deep learning models. It uses Tensorflow and Yolov7 models to detect dog objects and identify behavioral issues. The app includes a Dog Behavior Health button and a camera connected to a Raspberry Pi 3 board. The system also includes a dog collar and angular motion sensors. The project aims to provide a proactive tool for dog owners, trainers, and shelters to detect and treat behavioral issues, advance animal behavior research, and prevent health issues from obesity.