This article introduces Flexynesis, a deep learning-based bulk multi-omics integration method that streamlines data processing, feature selection, hyperparameter tuning, and marker discovery. With an emphasis on versatility and user-accessibility, the model allows users to choose either deep learning architectures or classical supervised machine learning methods. Regardless of the model chosen, Flexynesis provides a standardized input interface for single and multi-task training alongside evaluation for regression, classification, and survival modeling. In this study, Flexynesis was designed to construct a predictive model in either singular or multi-variable modeling and it was tested for its benchmark ability across a variety of cancers-related datasets. Results suggested that the choice between deep learning and baseline methods depends on the specific task, with Flexynesis performing well in some settings but not as reliable in others. In other words, a uniform approach is not the most effective strategy when it comes to deciding amongst models. Deep learning offers flexibility, the ability to be fine-tuned, and the ability to process the high quantities of data often seen in clinical research. In its current form, Flexynesis is not an alternative to single-cell-oriented tools. Rather, it is expected to be used in a supervised manner to investigate disease subtypes in a diagnostic or prognostic method. Flexynesis combines pre-existing deep learning models to form an innovative, user-friendly package, increasing multi-omic deep learning accessibility.