This project aims to develop an advanced multimodal Artificial Intelligence framework for precise time-series wind condition forecasting. By integrating data-driven approaches with physical constraints, the project seeks to provide a reliable tool for green energy harvesting, maritime safety, and sustainable urban development. Weather variables, including precipitation, atmospheric pressure, and wind speed are deeply intertwined with human activity. Among these, wind speed is a critical determinant for renewable energy acquisition, energy storage efficiency, and urban resilience. Existing research often falls short in achieving the precision required for modern applications, particularly as climate change introduces greater uncertainty into atmospheric patterns. This project addresses these gaps by leveraging multimodal AI techniques to enhance the accuracy and reliability of forecasting models. The study will utilize multi-source open-source data, including datasets from Zhongke Tianji, the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the National Ocean Service (NOS/NOAA) from US. The project will be executed in four primary stages: First of all, Data Acquisition and Understanding will be conducted, covering collecting data from various channels and analyzing its distribution and characteristics. Secondly, survey and Literature Review on historical research will be performed. An in-depth investigation into the current applications and bottlenecks of time-series models in weather forecasting will be conducted and summrized to provide guideline on our multimodel fusion methodology. Then, the multimodel fusion framework Development and Testing will be followed up. We will develop models based on the acquired data and perform cross-validation using both simulated and measured datasets. Lastly, paperworks, documentation and reporting. We will drafting research papers and providing regular project updates.