Precision agriculture (PA) is based heavily on the efficiency and reliability of wireless sensor networks (WSNs) to monitor the environment and crop parameters. Unfortunately, fault sensors in WSNs can lead to inaccurate data collection, which directly impacts decision-making and overall agricultural productivity. This study presents a comprehensive comparative analysis of popular machine learning (ML) models for fault detection and classification in wireless sensor networks, specifically relevant to PA applications. Using datasets with varying percentages of faulty data (10, 20, 30, 40, and 50%), we evaluate the performance of five popular ML models: Random Forest (RF), Support Vector Machines (SVMs), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN). Each model’s ability to detect and classify faults accurately is evaluated using accuracy and F1-score metrics. The impact of fault percentages on model performance is also analyzed to identify the most robust and reliable approaches for fault-tolerant WSN deployment. This study provides valuable insights into the selection of ML models for enhancing the reliability of WSNs in PA, contributing to sustainable and intelligent farming practices.