The objective of the proposed research is to develop adaptive solutions that significantly enhance energy efficiency and performance in data-driven sensor processing pipelines. This involves designing an energy-efficient sensor-ML pipeline interface with a mixed-signal hardware accelerator that processes raw analog sensor data to reduce data dimensionality before digitization, thereby lowering power consumption and data transfer overhead. Additionally, the research aims to create task- and sensor-specific neural network architectures tailored to sensor characteristics, such as radar or high-resolution cameras, by leveraging domain expertise to maintain accuracy while reducing complexity. Furthermore, it also seeks to implement adaptive sensor control through intelligent feedback mechanisms that dynamically adjust sensor configurations based on machine learning model requirements and real-time context, ensuring only the most relevant data is captured to minimize power usage. Finally, it proposes the integration of hardware-efficient online learning primitives that enable models to adapt to distribution shifts in dynamic environments, ensuring consistent performance on energy-constrained hardware.