The widespread adoption of Electric Vehicles (EVs) has intensified the need for efficient and scalable Electric Vehicle Charging Infrastructure (EVCI). A critical aspect of this development is the optimal siting of charging stations, which involves complex multi-criteria decision making based on spatial, economic, technical, and behavioral factors. This paper presents a comprehensive Systematic Literature Review on location analysis for EVCI planning, synthesizing findings from 91 peer-reviewed studies published between 2011 and 2024. We categorize and evaluate existing methodologies ranging from mathematical optimization models to Geographical Information System (GIS)-based and machine learning techniques and develop a comparative framework highlighting their strengths, limitations, and applicable contexts. In addition, we propose a unified taxonomy of influencing factors and a structured classification of decision-support approaches. Beyond summarization, the study identifies critical research gaps such as underexplored rural deployment models, limited real-time data integration, and inconsistent treatment of user behavior. To bridge these gaps, we suggest a hybrid GIS-Machine Learning (ML) conceptual framework and offer insights for future work aimed at scalable and equitable EVCI deployment. The outcomes provide urban planners, policymakers, and researchers with a roadmap for technically sound and sustainable infrastructure planning.