Proactive big data analytics in digital forensics emphasizes anticipating, detecting, and preventing cybercrimes before they occur by leveraging large-scale data analysis and intelligent automation. Research in this area focuses on developing predictive forensic frameworks that utilize big data platforms for early threat detection, behavioral modeling, and anomaly prediction. Key topics include machine learning–driven forensic intelligence, real-time event correlation, and pattern recognition across diverse data sources such as network logs, social media, and IoT devices. Other significant research areas involve integrating artificial intelligence for proactive evidence gathering, applying graph analytics for identifying hidden relationships, and employing deep learning models for trend forecasting in digital crime. Additionally, designing scalable data pipelines for continuous monitoring, implementing privacy-preserving predictive analytics, and enhancing forensic readiness through automated alert systems are emerging as vital directions for advancing proactive digital forensics in big data environments.