Honeybees play a vital role in maintaining the balance of ecosystems and supporting human life. Their primary contribution is pollination, which enhances agricultural productivity and biodiversity. Additionally, they produce valuable natural products such as honey, royal jelly, and propolis, which have nutritional and medicinal benefits. In recent years, increasing threats to bee populations have emphasized the need for improved beekeeping practices that ensure both the conservation of bees and the efficiency of hive management. Traditional beekeeping methods, while effective, are often labor-intensive and time-consuming. In contrast, precision beekeeping also known as smart beekeeping integrates technology to monitor and manage hives more efficiently. Artificial intelligence (AI)-based hive monitoring systems have emerged as powerful tools in this field. These systems use sensors and AI algorithms to collect and analyze data on temperature, humidity, bee activity, sound, and other environmental factors. They enable early detection of diseases, swarming, or other threats, allowing beekeepers to take timely action. Over the past decade, many studies have explored the use of machine learning and deep learning techniques in these systems. This chapter aims to provide a comprehensive overview of recent advancements and emerging trends in AI- supported hive monitoring, offering valuable insights to guide future research and technological innovation in modern beekeeping practices.