A comprehensive study of deep learning for soil moisture prediction explores 10 different network structures to enhance deep learning's potential for soil moisture prediction. The study compares the predictive abilities and computational costs of models across various soil textures and depths. It evaluates machine learning models like RF, ELM, and SVM, as well as deep learning models such as LSTM, 1D-CNN, and Transformer, along with hybrid models like CNN–LSTM, FA-LSTM, and GAN-LSTM. The study uses Shapley (SHAP) additive explanations and t-distributed stochastic neighbor embedding (t-SNE) visualization to analyze model interpretability and feature extraction. Results show that LSTM excels in temporal modeling, while FA-LSTM and GAN-LSTM improve accuracy through attention mechanisms and adversarial training. The study highlights the importance of appropriate model design for soil moisture prediction and provides insights into effective network design principles. The findings suggest that deep learning models, particularly LSTM and FA-LSTM, are effective for soil moisture prediction, with attention mechanisms and adversarial training enhancing performance. The study also emphasizes the importance of data preprocessing and model interpretability in soil moisture prediction tasks.A comprehensive study of deep learning for soil moisture prediction explores 10 different network structures to enhance deep learning's potential for soil moisture prediction. The study compares the predictive abilities and computational costs of models across various soil textures and depths. It evaluates machine learning models like RF, ELM, and SVM, as well as deep learning models such as LSTM, 1D-CNN, and Transformer, along with hybrid models like CNN–LSTM, FA-LSTM, and GAN-LSTM. The study uses Shapley (SHAP) additive explanations and t-distributed stochastic neighbor embedding (t-SNE) visualization to analyze model interpretability and feature extraction. Results show that LSTM excels in temporal modeling, while FA-LSTM and GAN-LSTM improve accuracy through attention mechanisms and adversarial training. The study highlights the importance of appropriate model design for soil moisture prediction and provides insights into effective network design principles. The findings suggest that deep learning models, particularly LSTM and FA-LSTM, are effective for soil moisture prediction, with attention mechanisms and adversarial training enhancing performance. The study also emphasizes the importance of data preprocessing and model interpretability in soil moisture prediction tasks.