<![CDATA[The rapid growth of the pet care industry highlights the urgent need for innovative technologies that ensure the health and well-being of companion animals. Conventional approaches to pet healthcare often rely on periodic veterinary visits, which may delay the detection of early symptoms and compromise preventive care. This study proposes an AI-driven Internet of Things (IoT) system for real-time monitoring and early disease detection in pets. The system integrates wearable IoT sensors to continuously capture physiological and behavioral data—such as heart rate, temperature, activity levels, and sleep patterns—and transmits them to a cloud-based platform. Machine learning algorithms are then employed to analyze these multimodal data streams, enabling predictive insights into potential health risks. Unlike previous research that focuses primarily on livestock monitoring, this work emphasizes companion animals, providing a tailored solution for urban households. A prototype implementation demonstrates the feasibility of combining IoT-enabled data collection with artificial intelligence for anomaly detection and early warning alerts. The proposed framework not only improves pet healthcare management but also supports veterinarians in delivering data-driven diagnoses. This approach introduces a scalable model for the next generation of smart pet healthcare systems, aligning with global trends in digital health and personalized animal care]]>
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