Cancer is a major global health challenge, with approximately 19.3 million new cases and 10 million deaths estimated by
2020. Laboratory advancements in cancer detection have transformed diagnostic capabilities, particularly through the
use of biomarkers that play crucial roles in risk assessment, therapy selection, and disease monitoring. Tumor histology,
single-cell technology, flow cytometry, molecular imaging, liquid biopsy, immunoassays, and molecular diagnostics have
emerged as pivotal tools for cancer detection. The integration of artificial intelligence, particularly deep learning and
convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. However, developing
countries face significant challenges including financial constraints, inadequate healthcare infrastructure, and limited
access to advanced diagnostic technologies. The impact of COVID-19 has further complicated cancer management
in resource-limited settings. Future research should focus on precision medicine and early cancer diagnosis through
sophisticated laboratory techniques to improve prognosis and health outcomes. This review examines the evolving
landscape of cancer detection, focusing on laboratory research breakthroughs and limitations in developing countries,
while providing recommendations for advancing tumor diagnostics in resource-constrained environments.