The complex integration of Industry 4.0 technologies into SMEs necessitates robust frameworks to address adoption barriers and enhance sustainability. The present study investigates the impact of artificial intelligence (AI), the Internet of Things (IoT), and blockchain on smart manufacturing, logistics, and sustainability in SMEs. Using a cross-sectional design, data were collected from 300 SMEs across manufacturing, logistics, and retail sectors through purposive sampling, focusing on technology adoption, and sustainability performance from 2018 to 2022. Data were analyzed using advanced machine learning models, including XG Boost and Random Forest, alongside Recursive Feature Elimination (RFE) for dimensionality reduction and quantile regression for an inferential analysis. Findings revealed that IoT adoption improved resource utilization efficiency, while blockchain enhanced ethical sourcing-furthermore, AI-driven predictive maintenance reduced operational downtimes. XG Boost achieved a Mean Squared Error (MSE), highlighting its superior predictive capability, while Random Forest achieved perfect fitness but risked overfitting. However, adoption varied significantly across firms due to financial and technical constraints, with SMEs reporting limited access to capital and skilled labor. This study underscores the need for policy interventions and targeted support for SMEs to bridge adoption gaps. The study advances the existing body of knowledge by highlighting the synergistic benefits of integrating Industry 4.0 technologies to enhance SME sustainability. Practical implications include policy recommendations for financial incentives, technical support, and capacity-building programs, empowering SMEs with actionable insights to overcome adoption barriers and achieve sustainable growth. These findings offer industry leaders and policymakers' actionable insights to drive SME transformation in Industry 4.0, empowering them to make a difference.