Deep learning (DL) is a type of machine learning capable of processing large quantities of data to provide analytic results based on a particular framework’s parameters and aims. DL is widely used in a variety of fields, including medicine. Currently, there are various DL-based prediction models for predicting cancer probability and survival. However, the specific problem is that no integrated system can predict cancer survival, probability, and presence in the medical patient’s samples. Therefore, this research investigates the latest literature in the field of DL-based cancer prediction models for predicting the cancer probability and the patient survival rate. The name of this proposed model is Multimodal Incremental Recurrent Deep Neural Network; it can perform the analysis, prediction, and diagnosis of cancer using multi-dimensional data processing. It can also predict the cancer possibility and survival using incremental recurrent neural networks. The components of the proposed taxonomy are Data, Prediction technique, and View (DPV). This research’s contribution is the critical analysis of the latest literature on the DL-based systems that can predict cancer and its outcomes. It provides a theoretical model that can predict the possibility, presence, and survival of cancer by processing multi-dimensional medical samples of the patient to make accurate predictions. We also highlight the importance of the proposed taxonomy.