Blood cancer, which includes leukemia, lymphoma, and myeloma, is a life-threatening disease characterized by abnormal blood cell proliferation. Early detection plays a crucial role in improving patient survival rates and treatment outcomes. This survey paper provides an in-depth analysis of existing blood cancer detection techniques, including conventional methods and emerging approaches like machine learning, deep learning, and biomarker-based diagnostics. The primary objective of this study is to evaluate the effectiveness, advantages, and limitations of these techniques by reviewing recent research advancements. Additionally, the paper highlights trends in integrating artificial intelligence and multi-omics data to enhance early-stage detection. By analyzing multiple studies and methodologies, this survey aims to provide a comprehensive understanding of current blood cancer detection strategies and their potential for clinical application.