Hydraulic fracturing relies on accurate pressure prediction for effective risk management and treatment evaluation. Deep learning models such as artificial neural networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, gated recurrent units, and CNN-LSTM offer promising avenues for this prediction. However, the choice of time step size significantly influences the models’ predictive power and engineering relevance. Understanding how different models respond to varying time step sizes remains a challenge. This study systematically compares these models for both single-step and multistep pressure predictions in hydraulic fracturing scenarios. By examining the impact of time step size on prediction accuracy and lag, the research sheds light on the tradeoffs between model complexity, dataset size, and prediction performance. The integration of multiple models aims to leverage their individual strengths while mitigating weaknesses, potentially enhancing overall prediction accuracy. This comprehensive analysis aims to inform practitioners and researchers about the optimal selection and deployment of deep learning models for pressure prediction, thus advancing the effectiveness and reliability of hydraulic fracturing operations.