Soft computing techniques have gained significant traction in the field of robotics due to their ability to handle imprecise and uncertain information effectively. This paper presents a comprehensive review of soft computing approaches employed in robotics, focusing on their state-of-the-art applications and future directions. Various soft computing paradigms, including fuzzy logic, neural networks, evolutionary algorithms, and swarm intelligence, are discussed in the context of robot control, navigation, path planning, learning, and optimization. The paper highlights the strengths and limitations of these techniques, along with their synergistic integration to enhance robotic capabilities. Furthermore, emerging trends and promising research directions in the intersection of soft computing and robotics are identified, such as explainable AI, human–robot interaction, multi-robot systems, and autonomous decision-making. This review aims to provide researchers and practitioners with insights into the current landscape of soft computing in robotics and inspire future advancements in this exciting interdisciplinary field.