Consumer electronics drones have become increasingly popular in recent years, offering various applications from aerial photography to search and rescue to delivery services. To enhance their capabilities and improve user experience, the integration of machine learning techniques has gained significant attention. This paper explores the various applications of machine learning in consumer electronics drones through mathematical frameworks, highlighting the potential benefits and challenges associated with their implementation. The key areas where machine learning can be leveraged, such as autonomous flight control, object detection and tracking, collision avoidance, and intelligent decision-making, are discussed. Furthermore, the impact of machine learning on drone hardware and software design is examined, encompassing considerations such as computational resources, data processing, and real-time responsiveness. The analysis of the current state of research and development in this domain provides valuable insights into the prospects and potential advancements of machine learning in consumer electronics drones.