Agrovoltaic systems increasingly rely on Internet of Things (IoT)-enabled heterogeneous wireless sensor networks (HWSNs) for real-time environmental monitoring. However, energy inefficiency and redundant data transmission remain critical challenges. This study proposes a hybrid optimization and learning framework, termed DOX-CMTabNetX, for efficient sensor deployment, clustering, and data analysis in agrovoltaic environments. The approach integrates a Brownian motion–enhanced Dingo Optimization algorithm for optimal cluster head selection using a multi-objective fitness function that jointly considers residual energy, communication distance, environmental zone priority, solar–moisture impact factors, and intra-cluster communication cost. To further improve efficiency, a redundancy reduction mechanism based on spatial and temporal correlation and Euclidean distance filtering is introduced to eliminate duplicate data transmissions.The proposed framework is evaluated against established benchmark models, including FAML, HBWCO, ST-IAOA-X, and FFO-TCN-BiGRU. The experimental results demonstrate that the proposed method significantly improves network performance, achieving up to 43.1%, 26.6%, 48.2%, and 40.9% higher throughput compared with the respective baseline models. Additionally, the integration of the MTabNetX classifier enables high-precision data analytics of Sensunuts module testbed generated dataset for classification accuracy in the range of 96–99%, representing an improvement of approximately 2–10% over existing methods. These results indicate that the proposed framework effectively reduces energy consumption, minimizes redundant transmissions, and enhances overall network lifetime and reliability, making it a robust solution for next-generation agrovoltaic IoT systems.