This bachelor's thesis explores the integration of machine learning (ML) with sensor fusion techniques to enhance spatial data accuracy in Extended Reality (XR) environments. With XR's revolutionary impact across various sectors, accurate localization in virtual environments becomes imperative. The thesis conducts a comprehensive literature review, highlighting advancements in indoor positioning technologies and the pivotal role of machine learning in refining sensor fusion for precise localization. It underscores the challenges in the XR field, such as signal interference, device heterogeneity, and data processing complexities. Through critical analysis, this study aims to bridge the gap in practical application of ML, offering insights into developing scalable solutions for immersive virtual productions. It offers insights into the practical integration of advanced machine learning techniques in XR applications, thereby providing valuable implications for technology development and user experience in XR. This contribution is not merely theoretical; it showcases practical applications and advancements in real-time processing and adaptability in complex environments, aligning well with existing research and extending it by addressing scalability and practical implementation challenges in XR environments. This study identifies key themes in the integration of ML with sensor fusion for XR, such as the enhancement of spatial data accuracy, challenges in real-time processing, and the need for scalable solutions. It concludes that the fusion of ML and sensor technologies not only enhances the accuracy of XR environments but also paves the way for more immersive and realistic virtual experiences.