Fast-evolving technologies have altered a variety of industries. The technologies like machine learning, deep learning, and reinforcement learning have influenced sectors from finance to healthcare, autonomous vehicles to smart cities, pushing the boundaries of what is possible and opening up new frontiers of research and applications due to which significant progress has been achieved in these domains. This chapter provides an overview of the latest frontiers of machine, deep, and reinforcement learning algorithms and their applications in cutting-edge domains. The first section of the chapter provides an overview of the methods and ideas behind machine learning, reinforcement learning and deep learning, while highlighting their uses and variations. Then we discuss the contemporary developments in machine learning, such as transfer learning, explainable AI, ensemble approaches, and federated learning, demonstrating their promise for resolving issues in cutting-edge fields. Then we discuss most recent advancements in deep learning, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), transformer models, and generative adversarial networks (GANs), as well as their uses in image recognition, speech recognition, and natural language processing. In this chapter, we also highlight the application of these learning algorithms in cutting-edge fields. We demonstrate how they might be utilized in fields like autonomous vehicles, where machine learning algorithms are applied in controlling and decision-making allowing self-driving automobiles to safely and effectively navigate challenging topography. We discuss its applications in healthcare, enabling personalized medicine and improving patient outcomes. Machine learning algorithms and their applications in financial industry for fraud detection, risk assessment, and stock forecasting, are also discussed. These algorithms and their role in cybersecurity for vulnerability assessment are elaborated. Recommendation systems and how the use machine learning algorithms can provide individualized recommendations for online advertising, and providing improving user experiences are highlighted. Machine learning and deep learning algorithms usage in smart cities is another important aspect in this chapter which is discussed. We also highlight the useful examples and real-world case studies that show how these algorithms are successfully applied in state-of-the-art applications, highlighting their efficacy and potential for handling challenging issues.