Keywords:
sales prediction
forecasting
machine learning
PRISMA-P
systematic literature review

  • Abstract

    Sales prediction plays a critical role across industries by enabling organizations to make informed decisions in inventory control, marketing, and strategic planning. This study aims to systematically review the application of machine learning (ML) techniques in sales forecasting, offering a synthesized understanding of prevailing algorithms, evaluation metrics, sector-specific challenges, and the distinction between theoretical and practical research. Adopting the PRISMA-P methodology, a structured literature search was conducted on the Scopus database, covering studies published from 2014 to 2024. From 206 initial publications, 66 were selected after applying inclusion criteria and quality assessment procedures. Findings show that ensemble learning techniques, particularly XGBoost and Random Forest, are most frequently used and consistently outperform traditional regression models in predictive accuracy. Common evaluation metrics include Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²), with RMSE being the dominant choice. Sector-specific insights reveal distinct forecasting challenges: the retail and e-commerce sectors grapple with demand volatility and rapidly shifting consumer behavior, the manufacturing industry contends with supply chain complexities and economic disruptions, while the energy sector deals with high-frequency fluctuations and data granularity issues. Moreover, this review identifies a clear divide between model-driven studies focused on algorithmic optimization and problem-based research that integrates contextual variables such as consumer sentiment, macroeconomic trends, and operational constraints to enhance real-world applicability. The review highlights that hybrid and adaptive approaches—especially those incorporating external data and collaborative industry inputs—offer promising pathways toward more actionable and scalable forecasting solutions. By bridging the gap between technical performance and practical deployment, this systematic literature review underscores the importance of aligning ML methodologies with real-world business contexts to advance the effectiveness of sales prediction systems.

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Saptadi, S., Budiawan, W., Fadhila, R. N., Sumbodo, R. K., & Saputra, D. W. N. (2025). Machine learning applied to sales prediction modeling: A systematic literature review. Multidisciplinary Reviews, 9(3), 2026127. https://doi.org/10.31893/multirev.2026127
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