Internet of Things (IoT) applications are conventionally classified into small-scale and large-scale deployments. Small-scale applications, such as smart homes, typically span 0–100 meters and utilize short-range communication technologies like Zigbee and Bluetooth Low Energy (BLE). In contrast, large-scale applications, exemplified by Microsoft’s FarmBeats, extend across 1–5 kilometers and rely on long-range radios such as LoRa and SigFox. Recently, a new and distinct class of deployments, termed mesoscale IoT applications, has emerged. These operate over intermediate distances (approximately 0.1 to 1 kilometer) and often repurpose either short-range or long-range radios beyond their optimal design ranges. Mesoscale applications face unique challenges, including unpredictable link quality, lack of dedicated radio infrastructure, and environmental variability, which are not adequately addressed by existing protocols designed for small- or large-scale systems. To address this gap, this work explores the integration of machine learning techniques into IoT wireless systems to enable adaptive decision-making, optimize protocol behavior, and improve end-to-end network performance in mesoscale IoT deployments. To address the challenges of mesoscale IoT applications, this dissertation introduces three complementary systems: MARS, COMNETS, and EDRP. MARS addresses the limitations of dedicated radio technology by demonstrating that a multi-radio architecture is beneficial for mesoscale deployments. It tackles theproblem of radio selection by incorporating novel machine learning techniques to optimize throughput in multi-radio IoT networks. Building on the cost-sensitivity challenges identified in MARS, COMNETS presents an interpretable, decision tree-based framework that further improves throughput while minimizing high-cost mispredictions. It also enables system designers to derive meaningful insights from the machine learning model. Finally, EDRP is developed to address thechallenge of efficient bulk data transmission in mesoscale IoT networks. Extensive in-field experiments demonstrate that these contributions significantly enhance the performance of mesoscale IoT networks
