This article presents an innovative open -source software named ModelFLOWs-app, 1 written in Python, which has been created and tested to generate precise and robust hybrid reduced order models (ROMs) fully data -driven. By integrating modal decomposition and deep learning in diverse ways, the software uncovers the fundamental patterns in dynamic systems. This acquired knowledge is then employed to enrich the comprehension of the underlying physics, reconstruct databases from limited measurements, and forecast the progression of system dynamics. The hybrid ROMs produced by ModelFLOWs-app combine experimental and numerical databases, serving as highly accurate alternatives to numerical simulations. As a result, computational expenses are significantly reduced, and the models become powerful tools for optimization and control in various applications. The exceptional capability of ModelFLOWs-app in developing reliable data -driven hybrid ROMs has been demonstrated across a wide range of applications, making it a valuable resource for understanding complex nonlinear dynamical systems and providing insights in diverse domains. This article presents the mathematical background, as well as a review of some examples of applications. Program summary Program title: ModelFLOWs-app CPC Library link to program files: https://doi .org /10 .17632 /49tzcc8sf3 .1 Developer's repository link: github.com/modelflows/ModelFLOWs-app Licensing provisions: MIT license Programming language: Python Supplementary material: Tutorial, example datasets. Nature of problem: ModelFLOWs-app is an open -source Software for data post -processing, patterns identification and development of reduced order models using modal decomposition and deep learning architectures. ModelFLOWs-app provides its users with a user-friendly interface to efficiently identify patterns in data, reconstruct data by repairing or enhancing it, and make predictions based on the available data. Solution method: ModelFLOWs-app methodological framework is formed by two big modules: Module 1 uses modal decomposition methods, and Module 2 is formed by hybrid machine learning tools, which combine modal decomposition with deep learning architectures. Each module solves three different applications: (1) patterns identification, suitable to study the physics behind the data analysed; (2) data reconstruction, capable to reconstruct two- or three- dimensional databases from a set of selected points, using data from sensors, or repairing missing data; (3) data forecasting, which builds reduced order models (ROMs) to predict the spatiotemporal evolution of the signal analysed.