<p>This research introduces a novel approach by combining Long Short-Term Memory (LSTM) networks with Response Surface Methodology (RSM) to optimize and predict the performance of a plate heat exchanger for industrial applications. While both techniques have been individually applied in heat exchanger studies, this work demonstrates their integration to enhance performance prediction and optimization. Experimental data were collected across variable hot fluid flow rates (0.5–1.4 l/min), cold fluid flow rates (1–3 l/min), and feed temperatures (50–60 °C). The optimal heat exchanger effectiveness was found to be 57.41%. The LSTM model outperformed the RSM model, achieving an R² value of 0.99, compared to R² of 0.9022 for RSM. Additionally, the LSTM model achieved a Mean Absolute Error (MAE) of 1.44, Mean Squared Error (MSE) of 3.49, and Root Mean Squared Error (RMSE) of 1.87, while the RSM model had MAE of 1.19, MSE of 2.24, and RMSE of 1.49. These findings highlight the significant potential of combining RSM with LSTM for accurately predicting heat exchanger performance and optimizing its effectiveness. This study offers a crucial data-driven strategy that not only improves prediction accuracy but also provides valuable insights into the optimization of heat exchangers in industrial applications, offering practical benefits for energy efficiency and system performance.</p> Graphical abstract <p></p>