Two algorithms based on machine learning neural networks are proposed—the shallow learning (S‐L) and deep learning (D‐L) algorithms—that can potentially be used in atmosphere‐only typhoon forecast models to provide flow‐dependent typhoon‐induced sea surface temperature cooling (SSTC) for improving typhoon predictions. The major challenge of existing SSTC algorithms in forecast models is how to accurately predict SSTC induced by an upcoming typhoon, which requires information not only from historical data but more importantly also from the target typhoon itself. The S‐L algorithm composes of a single layer of neurons with mixed atmospheric and oceanic factors. Such a structure is found to be unable to represent correctly the physical typhoon‐ocean interaction. It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D‐L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. Therefore, it produces a stable crescent‐shaped SSTC distribution, with its large‐scale pattern determined mainly by atmospheric factors (e.g., winds) and small‐scale features by oceanic factors (e.g., eddies). Sensitivity experiments reveal that the D‐L algorithms improve maximum wind intensity errors by 60–70% for four case study simulations, compared to their atmosphere‐only model runs. Plain Language Summary Forecasting accuracy with respect to storm track and intensity are two important factors for evaluating typhoon models. While 24‐h forecast errors of typhoon track have steadily improved to an order of 50 km, the prediction of typhoon intensity has remained one of the major challenges during the last decade. In this study, two algorithms based on machine‐learning neural networks are proposed‐the shallow learning (S‐L) and deep learning (D‐L) algorithms‐that can potentially be used in atmosphere‐only typhoon forecast models to provide flow‐dependent typhoon‐induced sea surface temperature cooling (SSTC) for improving typhoon predictions. Key Points A parameterization scheme based on deep learning neural network is proposed for atmosphere‐only typhoon forecast models The deep learning algorithm is designed to combine information from historical data and the target typhoon The scheme based on the deep learning algorithm achieves an equivalent representation as the fully coupled model