The field of forensic science is constantly changing, and one important technique to keep up with these changes is machine learning (ML). The present review paper addresses current knowledge gaps and describes the uses of machine learning (ML) in various forensic disciplines, thoroughly analysing the urgent need to integrate ML into forensic research. The study examines the foundations of machine learning, highlighting its application to pattern recognition, categorization, and forecasting. The paper details the role of ML in several fields, including digital forensics, biology, genetics, chemistry, and toxicology. Methods such as ensemble learning, support vector machines (SVM), and convolutional neural networks (CNNs) show promise in addressing problems specific to their respective fields. The importance of ethical and legal factors cannot be overstated. Data privacy, bias, and opacity issues with ML algorithms are all covered. The study discusses the necessity of testing and validation requirements in legal frameworks, upholding chain of custody, standards for expert witnesses, and the admissibility of ML-generated evidence. Obstacles such as skewed datasets, confidentiality, legal compliance, limited resources, and educational disparities are emphasized, underscoring the need for proactive measures and cooperative endeavours. To maintain adherence to justice, transparency, and confidentiality standards while navigating the developing convergence of ML and forensic science, it is imperative to strike a balance between innovation and ethical responsibility.