Accurate localization is crucial for numerous applications, spanning from navigation systems to indoor positioning and asset tracking. However, achieving precise localization remains challenging, especially in environments where traditional positioning technologies face limitations. To address this challenge, this paper proposes a novel approach: the fusion of multiple positioning technologies. By integrating data from GPS, Wi-Fi, Bluetooth, RFID, and other sensors, our framework aims to enhance localization accuracy, robustness, and adaptability across diverse environments. We present a comprehensive fusion algorithm that combines geometric, probabilistic, and machine learning techniques, while incorporating context-awareness mechanisms for adaptive localization. Through simulations and real-world experiments, we demonstrate the effectiveness of our fusion framework in improving localization accuracy and resilience to environmental factors. This research contributes to advancing the state-of-the-art in localization technologies and opens avenues for innovative applications in transportation, healthcare, retail, and beyond.