Annual financial planning is a critical aspect of sustainable economic management for organizations, governments, and institutions. Traditional forecasting methods, such as linear regression and ARIMA, often fall short in capturing the non-linear and dynamic nature of real-world financial data. These limitations hinder the accuracy and adaptability required for proactive fiscal decision-making. This research proposes a predictive framework leveraging deep learning models—specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks—for enhanced annual expense forecasting. The proposed system is designed to process historical financial datasets, identify temporal patterns, and predict future expenditures with high precision. A comparative analysis was conducted to evaluate the performance of LSTM and GRU models against classical statistical approaches using real-world financial datasets. Experimental results demonstrate that the deep learning models, particularly LSTM, significantly outperform traditional methods in terms of prediction accuracy, robustness, and responsiveness to seasonal variations in expenditure. The study establishes the potential of advanced neural networks in automating and optimizing financial planning processes, ultimately aiding in resource allocation and policy formulation. The findings contribute to the growing field of AI-driven financial analytics and provide a foundation for scalable, data-informed budgeting systems.

Keywords: Deep learning, financial forecasting, LSTM, GRU, time series prediction, annual expense management