Drones for IoT applications have emerged as a transformative approach for efficient sensing and data collection. This chapter presents a comprehensive cost–benefit analysis of utilizing drones equipped with various sensors—environmental, imaging, and communication—for data gathering in IoT networks. By leveraging real-time and batch data collection methods, drones offer significant advantages in connectivity and data transmission. Initial acquisition costs of drones range from $500 to $50,000 depending on capabilities, with annual maintenance expenses averaging $2,000 per unit. Operational costs, influenced by energy consumption and personnel training, add an additional $1,500 per drone per year. Compliance costs, including licensing, permits, and insurance, contribute approximately $1,000 annually. On the benefit side, drones significantly enhance operational efficiency and speed, enabling rapid deployment and real-time data availability. The ability to access remote and hazardous areas increases versatility, while high-resolution data capture improves accuracy and reliability. Economic analysis models, such as Cost–Benefit Analysis (CBA), Return on Investment (ROI), and Life Cycle Cost Analysis (LCCA), are applied to evaluate financial viability. Studies indicate that drones can reduce operational costs by 30–50% and improve data quality by 20–40% compared to traditional methods. Challenges in this analysis include data uncertainty, technological risks, and ethical considerations. However, advancements in drone technology, AI integration, and improved data analytics are mitigating these issues, leading to cost reductions and enhanced benefits. Comparative analysis with ground-based and satellite-based methods shows that drones offer superior flexibility and cost-effectiveness, making them a preferred choice for modern IoT applications. Future trends suggest continued growth and adoption, driven by innovations and evolving regulatory frameworks, underscoring the strategic importance of integrating drones into IoT networks for optimized outcomes.