Latest research in Proactive Big Data Analytics for Digital Forensics focuses on integrating machine learning, artificial intelligence, and big data techniques to anticipate, detect, and respond to cyber threats more efficiently. Studies highlight frameworks that utilize supervised learning models, deep learning algorithms, and adversary emulation to identify anomalies, trace malicious activity, and perform predictive analysis across large-scale datasets. Research emphasizes automating forensic processes, enabling proactive monitoring, and collecting digital evidence from endpoints and networked systems while maintaining integrity and admissibility. Overall, these advancements aim to modernize digital forensic investigations by providing scalable, intelligent, and proactive methodologies for early threat detection and effective cybercrime prevention.