Paper Title
A Framework for Maritime Emission Prediction and Optimal Berth Scheduling to Reduce Environmental Impact

Abstract
Despite being essential to international trade, the maritime sector greatly contributes to environmental pollution through carbon emissions and ineffective port operations. Emission forecasting and effective berth scheduling are two significant maritime logistics issues that are addressed in this study using an integrated approach that combines machine learning and optimization algorithms. The goal of the project is to create a predictive model that uses real-time data from environmental variables, fuel consumption, and vessel movements to accurately estimate maritime emissions. To improve the accuracy of emission forecasts, machine learning techniques will be applied to the analysis of both historical and current data. Furthermore, this study provides a framework for berth scheduling optimization that can meet different shipping demands while ensuring lower fuel consumption and fewer delays. Port operations will be streamlined using modern optimization algorithms that maintain both environmental sustainability and economic viability. By integrating these two components, the proposed approach enhances decision-making for maritime stakeholders and makes it possible to create informed scheduling and emission control plans. The results will reduce the carbon footprint of maritime logistics while improving operational effectiveness. This study is a significant step toward sustainable and intelligent maritime transportation, which will benefit the global economy and environment. Keywords - Maritime Industry, Port Operations, Berth Assigning, Emission, Sustainability.