<p>Wireless Sensor Networks (WSNs) play a crucial role in smart agriculture enabling real-time environmental monitoring and data-driven decision-making. However, WSN efficiency is often compromised by energy constraints, suboptimal clustering, and inefficient cluster head selection, which negatively impact network lifespan and data reliability. To address these challenges, this paper presents a New Hybrid Adaptive Design Optimization (HAOD) algorithm that integrates Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) for dynamic clustering and energy-efficient sensor management. The proposed approach optimizes network topology by leveraging dimensionality reduction techniques to determine the optimal number of clusters while dynamically adjusting the clustering process based on sensor placement, energy consumption, and connectivity. Additionally, an adaptive weighting mechanism is incorporated to refine sensor operation modes in real-time, ensuring balanced energy distribution and prolonged network lifespan. Unlike conventional clustering methods that rely on static configurations, our model enables a self-organizing and adaptive WSN structure that enhances both energy efficiency and data accuracy. Simulation results in a smart agriculture setting demonstrate that the proposed method significantly outperforms traditional clustering techniques by reducing energy consumption, improving cluster stability, and extending network longevity. This research contributes to the advancement of sustainable and intelligent WSN architectures, offering a scalable and energy-conscious solution for precision agriculture applications.</p>