Big data forensic analysis focuses on investigating and interpreting massive, heterogeneous datasets generated across digital systems to uncover evidence of cyber incidents or malicious activities. Research in this domain explores scalable forensic frameworks that leverage distributed computing platforms such as Hadoop and Spark for efficient evidence processing and correlation. Key areas include automated evidence extraction, pattern mining for cybercrime detection, and anomaly identification using machine learning and deep learning models. Other important topics involve provenance tracking, secure and tamper-proof evidence storage using blockchain, and cross-domain forensic data fusion from cloud, IoT, and mobile sources. Furthermore, privacy-preserving forensic analytics, visualization techniques for large-scale forensic insights, and the integration of explainable AI for transparent forensic reasoning are emerging as critical directions. Advancing these areas enables faster, more accurate, and legally admissible forensic analysis in today’s complex big data environments.