Accurate assessment of crop nutritional status is critical for effective farm management, affecting both environmental sustainability and economic viability. Nitrogen, an essential nutrient for plant growth, is critical in detecting crop health and making fertilization decisions. However, standard nitrogen level estimation methods frequently include labor-intensive and environmentally dangerous laboratory analyses. In response, this study investigates the possibilities of modern technologies, notably machine learning (ML) and remote sensing, for improving nitrogen estimate in crops. Remote sensing, which uses sensors mounted on satellites, drones, or other airborne platforms, provides a non-destructive and efficient alternative to traditional methods for obtaining extensive spectral data. Machine learning techniques improve upon this approach by processing massive amounts of data to uncover significant patterns and relationships. Although previous studies have primarily relied on vegetation indices generated from spectral observations, this study provides an alternate technique. By auto-encoding raw spectral data, machine-learned features are developed as an alternative to vegetation indices, providing a new perspective on leaf nitrogen content (LNC) estimation. To test performance, a number of machine learning algorithms are examined, including random forest, support vector machines, and extreme gradient boosting. Our findings suggest that the autoencoder-based methodology outperforms established methods, highlighting its potential for reshaping nitrogen estimate in agriculture.