Artificial intelligence (AI) and machine learning (ML) technologies hold transformative potential for construction SMEs, yet only 35% of these firms in Beijing report adoption compared to 60% among larger firms (China Construction Industry Association in Annual report on technology adoption in the construction sector [2]). This study integrates the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework to examine AI/ML adoption, focusing on perceived usefulness (PU), perceived ease of use (PEU), and AI/ML adoption readiness (AMAR). Novel to this research is the mediation analysis of organizational and environmental factors, such as leadership commitment, policy incentives, and market competition, in shaping adoption outcomes. Data collected through a validated survey instrument adopted from well-established scales will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings will provide critical insights for addressing Beijing-specific challenges, such as limited digital infrastructure and regulatory barriers, offering actionable strategies for enhancing adoption rates and enabling SMEs to achieve operational efficiency and competitiveness.