Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications such as computer vision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and IoT systems such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based technologies, accelerated with the improvement of hardware technologies. The cost of a deadline (required time constraint) missed by ML/DL algorithms would be catastrophic in these safety-critical systems. However, ML/DL algorithm-based applications have more concerns about accuracy than strict time requirements. Accordingly, researchers from the real-time systems community address the strict timing requirements of ML/DL technologies to include in real-time systems. This paper will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy vs. execution time trade-off policies of ML algorithms, and security & privacy of learning-based algorithms in real-time IoT systems.