Digital forensic investigations are critical in modern law enforcement, cybersecurity, and legal proceedings. Ensuring digital evidence’s accuracy, integrity, and reliability is paramount in these contexts. This review article explores the challenges and best practices associated with quality control in digital forensic investigations and the emerging technologies that are reshaping the field. The article begins by discussing the foundational concepts of quality control in digital forensics, emphasizing the need for standardized procedures, documentation, and validation techniques. It delves into the potential sources of errors and bias that can arise during the acquisition, preservation, analysis, and presentation of digital evidence. It highlights the importance of continuous monitoring and review to mitigate these risks. The review article further examines the evolving landscape of digital forensic tools and technologies advancing quality control efforts. It covers advancements in data acquisition methods, including live forensics and memory analysis, and discusses the role of artificial intelligence and machine learning in automating quality control processes. The integration of Blockchain and cryptographic techniques for ensuring the integrity of digital evidence is also explored. In addition, the article addresses the challenges and opportunities presented by cloud computing, IoT devices, and the proliferation of digital data sources. It emphasizes adaptability and agility in quality control approaches to accommodate the changing digital landscape. Through a comprehensive analysis of established practices and emerging technologies, this review article offers practitioners, researchers, and policymakers’ insights into enhancing the reliability and trustworthiness of digital forensic investigations. By adopting robust quality control measures and embracing innovative technologies, the digital forensics community can ensure its findings hold up to scrutiny in the courtroom and beyond.