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This chapter presents an in-depth comparison of three prominent Deep Learning Frameworks: TensorFlow, PyTorch, and Keras. Starting with a brief introduction to the impact of these frameworks on artificial intelligence and their role in developing intelligent systems, the text dives into individual analyses of TensorFlow, PyTorch, and Keras. Each framework’s overview, strengths, weaknesses, and specific applications are discussed. TensorFlow’s scalable and flexible architecture is highlighted, but its steep learning curve is noted. PyTorch is commended for its user-friendly, dynamic approach, while its deployment capabilities are considered inferior to TensorFlow’s. Keras is praised for its simplicity, perfect for beginners, and quick prototyping but lacks performance for large-scale projects. Finally, guidance is provided on choosing the most suitable framework, considering the project’s size, the need for customization, and the desired deployment environment.