Background: The rapid advancements in Artificial Intelligence (AI) have paved the way for innovative applications in the field of healthcare, particularly in initial finding and diagnosis of cancer. This study explores integration of AI techniques, specifically machine learning algorithms, to enhance efficiency and accuracy of cancer detection methods. Aim: The primary objective of our current research is to conduct the relative analysis of numerous machine learning algorithms applied to initial discovery and analysis of cancer. By assessing and associating performance of those algorithms, study aims to recognize the most effective and dependable approach for enhancing the diagnostic accuracy and efficiency in the realm of cancer detection. Methods: The study employs a comprehensive methodology involving the collection and preprocessing of diverse datasets related to different types of cancer. Various machine learning algorithms, such as support vector machines, neural networks, decision trees, and ensemble methods, are applied and optimized in the implementation process. Performance metrics like sensitivity, specificity, and accuracy are used to evaluate the effectiveness of each algorithm in early cancer detection. Results: The comparative analysis reveals varying degrees of performance across the implemented machine learning algorithms. The results highlight the strengths and limitations of each algorithm in terms of sensitivity, specificity, and overall accuracy. By understanding these differences, healthcare professionals and researchers can make informed decisions regarding the selection and execution of AI-based tools for cancer recognition. Conclusion: The integration of AI in initial detection and diagnosis of cancer holds immense potential for improving patient outcomes. The comparative analysis of machine learning algorithms conducted in the current study provides valuable insights into their effectiveness and performance characteristics. The findings contribute to the ongoing efforts to optimize AI applications in healthcare, guiding future research and development in field of cancer diagnostics.