The integration of robotics, artificial intelligence (AI), and sustainability is reshaping construction management from a predominantly project-centered discipline toward a data-driven, adaptive, and optimization-oriented system. However, the effective integration of these technologies requires more than the independent deployment of autonomous equipment or intelligent algorithms. It requires a framework capable of coordinating operational decisions, resource allocation, uncertainty, mobility, and environmental objectives. This research develops a conceptual framework for next-generation construction management by synthesizing the provided literature on clustering, fuzzy programming, multi-level optimization, electric-vehicle routing, charging navigation, and intelligent transportation systems. The methodology uses a structured conceptual synthesis to translate these computational principles into construction-management functions such as robotic task allocation, autonomous material movement, energy-aware equipment scheduling, uncertainty management, and sustainability optimization. The resulting framework positions AI as the decision-intelligence layer, robotics as the execution layer, and sustainability as an integrated performance objective. The analysis indicates that fuzzy and multi-objective approaches are particularly valuable where construction decisions involve conflicting objectives and incomplete information, while clustering and routing methods can support spatial organization and autonomous mobility. The framework further demonstrates that sustainable construction management should be treated as a dynamic optimization problem rather than a post-project environmental assessment. The proposed model contributes a theoretically grounded basis for integrating intelligent automation with operational and environmental decision-making while recognizing limitations related to data quality, interoperability, uncertainty, and contextual transferability.