<p>Cloud computing has emerged as a transformative technology that enables efficient resource management and dynamic scalability across distributed environments. With the exponential growth of cloud-based applications and services, effective resource allocation techniques have become critical to ensuring optimal performance, cost efficiency, and service reliability. Resource allocation encompasses various challenges, including load balancing, task scheduling, energy efficiency, and Quality of Service (quality of service) optimization. This systematic literature review (SLR) aims to analyze and categorize existing resource allocation techniques in the cloud computing environment, with a focus on optimization strategies, heuristic algorithms, and machine learning-based approaches. A representative set of 100 research articles, published between 2014 and 2025, has been selected using well-defined inclusion and exclusion criteria. The study examines the strengths and limitations of different methodologies, identifies research gaps, and highlights emerging trends in resource allocation. Furthermore, it provides insights into the effectiveness of various algorithms, evaluates performance metrics, and discusses potential advancements in adaptive and intelligent resource management solutions. This review work contributes to the field by offering a comprehensive overview of existing approaches and paving the way for future research directions in cloud computing resource allocation.</p>