The global demand for rice necessitates advancements in paddy cultivation practices, particularly in addressing nutrient deficiencies that directly impact yield and quality. The present work investigates the applications of machine learning (ML) methods for detecting nitrogen deficiency in paddy crops through leaf image analysis. Traditional methods of identifying nutrient deficiencies are often found to be more labor cost intensive and time-consuming and this leads to an idea of highlighting the need for innovative solutions to improve the productivity of crops. Our study employs various ML models that are comprised of Convolutional Neural Networks-CNNs, Support Vector Machines-SVMs, and Random Forest-RF to analyze high-resolution leaf images and accurately classify nitrogen-related conditions of the crops. Key challenges addressed include the variability of deficiency symptoms and the integration of ML techniques into practical agricultural workflows. A dataset of 56,000 paddy leaf images was utilized, with performance metrics indicating that CNNs outperformed other models, achieving an accuracy of 94%. This demonstrates the potential for ML to enhance precision agriculture, enabling timely and informed interventions to improve crop management. The findings suggest that integrating advanced imaging and ML techniques can significantly streamline the identification of nutrient deficiencies, ultimately supporting sustainable farming practices.