| Abstract | Abstract
In today's business and commerce, weather forecasting is essential for operational decision-making and strategic
planning. Due to their limited capacity to develop nonlinear atmospheric dynamics and computational complexity,
traditional numerical weather prediction models often encounter difficulties. Data-driven, scalable, and highly accurate
forecasting solutions have been made possible by recent developments in artificial intelligence and machine learning.
With a focus on incorporating intermediate meteorological data to increase accuracy, this paper provides an overview
of sophisticated machine learning methods for weather prediction, such as LSTM, CNN, hybrid CNN–LSTM
architectures, attention mechanisms, and ensemble models. The paper also examines important business applications
in the fields of agriculture, supply chain management, energy, retail, insurance, and tourism, showing how AI-driven
weather intelligence promotes resource efficiency, risk reduction, and well-informed business decision-making.
Keywords: Weather Forecasting, Machine Learning, Deep Learning Models, Business Intelligence, Commercial
Decision-Making.
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