Research on Localization Algorithms in Wireless Sensor Networks (WSNs) focuses on accurately determining the positions of sensor nodes to enable efficient data routing, monitoring, and event tracking. Since GPS-based methods are often costly and energy-intensive, recent studies emphasize range-free and range-based localization techniques that use parameters like hop count, signal strength (RSSI), and time of arrival (TOA). Machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization and differential evolution have been widely adopted to enhance accuracy, robustness, and scalability in complex environments. Hybrid localization models combining anchor-based and anchor-free approaches also improve precision while reducing computational overhead. Overall, advancements in localization algorithms aim to achieve high positioning accuracy, low energy consumption, and adaptability for real-time applications in dynamic and large-scale WSN deployments.