Sustainable logistics is crucial for addressing vulnerabilities within organizations, where achieving operational resilience relies heavily on accurate risk evaluation and cost prediction. However, in the current dynamic context, forecasting logistics risk costs presents considerable challenges for organizations aiming to mitigate financial and sustainable issues. In this regard, this study proposes an advanced forecasting framework using an adaptive fuzzy neuro-inference system (AFNIS) to predict sustainable logistics risk costs. The proposed ANFIS leverages its adaptive nature to continuously refine predictions in response to evolving environmental factors and emerging sustainable risks. By incorporating various dimensions of logistic risk and multiple inputs, such as sustainable standards, logistical requirements, organizational features, logistics factors, and ISO 14001 standards, this study introduces a learning approach for risk cost mitigation. Through systematic evaluation of logistics risks, the model facilitates risk prioritization based on anticipated impact and associated costs. This enables the development of proactive strategies to mitigate sustainable logistic disruptions effectively. Consequently, implementing the proposed framework offers a structured methodology for continuous monitoring and rigorous evaluation of logistics risks using digital solutions, thereby enhancing decision-making.