Deep learning technologies are transforming the agriculture sector by enabling smart farming. In this research, a deep learning model was developed to predict the most suitable crop for a given soil type by analyzing a variety of parameters. This method is consistent with the primary purpose of smart farming: to develop systems that enhance resource efficiency and encourage sustainable agriculture by providing data-driven recommendations. Feedforward Neural Network (FNN) was employed and trained on a crop recommendation dataset. This dataset includes key features such as soil nutrient levels (nitrogen, phosphorus, and potassium), temperature, humidity, pH, and rainfall, which are essential for determining optimal crop growth conditions. The FNN model takes these features as input and is trained to learn the relationships between them and the target outputs (crop labels). The efficiency of the FNN model is evaluated using performance metrics suitable for classification tasks, such as Accuracy, Precision, and Recall. The model achieved an accuracy of 92%, precision of 90%, and recall of 89%, demonstrating a strong ability to recommend the most suitable crop for a given soil. This technique has a variety of practical uses, allowing farmers to make informed decisions that increase agricultural output and health while reducing resource waste in real-world farming environments. The integration of deep learning, as demonstrated by this research, represents a significant step toward improving resource management and crop health in the agricultural sector.