The emergence of machine learning (ML) and big data analytics has fundamentally transformed how businesses forecast future trends, make strategic decisions, and optimize operations. This empirical study investigates the impact of ML and big data analytics on business prediction accuracy and decision-making effectiveness across five key dimensions: data infrastructure and quality, analytical capabilities and tools, predictive model sophistication, organizational data culture, and integration with business processes. Using a quantitative research design, primary data was collected from 200 business professionals through a structured questionnaire using simple random sampling. Responses were measured on a 5-point Likert scale, and the instrument demonstrated high reliability with Cronbach's alpha coefficients exceeding 0.70. Statistical analysis was conducted using SPSS, employing both descriptive and inferential statistics.

The findings reveal that all five dimensions significantly impact business prediction accuracy at the 5% significance level. Predictive model sophistication emerged as the strongest predictor (β=0.524), followed by analytical capabilities and tools (β=0.489), data infrastructure and quality (β=0.443), integration with business processes (β=0.421), and organizational data culture (β=0.387). All effect sizes were large based on Cohen's f², indicating substantial practical significance. Consequently, all null hypotheses were rejected in favor of the alternative hypotheses.

The study concludes that successful implementation of ML and big data analytics for business predictions requires a holistic approach encompassing robust data infrastructure, advanced analytical tools, sophisticated predictive models, supportive organizational culture, and seamless integration with business workflows. These findings provide valuable insights for business leaders, data scientists, and policymakers seeking to enhance forecasting capabilities through strategic deployment of ML and big data technologies..