Agriculture plays an important role in ensuring food security for the growing global population. However, weed growth is one of the major challenges faced by farmers because weeds compete with crops for essential nutrients, sunlight, water, and soil space. Traditional weed detection methods mainly rely on manual observation and uniform herbicide application, which are labor-intensive, time-consuming, and often inaccurate. In recent years, machine learning and image processing techniques have emerged as powerful tools in precision agriculture. This research proposes a machine learning-based weed prediction system that automatically detects and predicts weeds from agricultural crop images. The system applies image preprocessing, feature extraction, and machine learning classification techniques to distinguish crops from weeds. Algorithms such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest can be used to classify plant images based on visual features such as leaf shape, texture, and color patterns. The proposed system helps farmers detect weeds at an early stage, enabling timely intervention and reducing excessive herbicide usage. Experimental results demonstrate that the machine learning model achieves high accuracy and improves decision-making in weed management. The system contributes to precision agriculture by reducing manual labor and improving agricultural productivity.