<![CDATA[The complexity of the contemporary business environment demands a more precise approach to cost prediction and budgeting to maintain an organizational competitive advantage. This study examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in improving the accuracy of cost prediction and budgeting through a Systematic Literature Review of ten high-quality journal articles published for the period 2020-2025. The PRISMA methodology was applied with the stages of identification of 309 articles, elimination of 102 duplicates, screening of 207 articles, evaluation of 112 full-texts, and final selection of 10 articles that met the eligibility criteria. The results of the literature synthesis revealed that deep learning models such as LSTM and ensemble methods such as XGBoost achieved superior accuracy with a MAPE of 2.88-9% and an R² score of 0.90-0.95, significantly outperforming conventional methods. The effectiveness of AI techniques is context-specific with optimal deep learning for high complexity, classical machine learning for organizations with infrastructure limitations, and regression models for transparency priorities. Implementation challenges include data quality, the black box nature of algorithms, and substantial investment requirements. Optimization strategies include a hybrid workflow approach, gradual implementation, and data governance strengthening. The integration of AI with big data analytics enables dynamic budgeting that is adaptive to market volatility, providing strategic implications for financial management practitioners in optimizing resource allocation and data-driven decision-making.]]>
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