With the rapid advancement of technology, the increasing integration of machine learning (ML) and artificial intelligence (AI) algorithms into decision-making processes has raised significant concerns about algorithmic biases. This survey paper aims to analyze the various biases identified and categorized within this domain and examine their impact through real-life examples. The paper explores the ramifications of biases in ML algorithms, highlighting their consequences in real-world applications. It also evaluates current methods available for detecting, quantifying, and mitigating these biases. By critically analyzing these approaches, the study provides insights into their strengths and limitations. Furthermore, the paper delves into techniques for bias reduction across different stages of the ML pipeline, including pre-processing, in-processing, and post-processing methods. Emphasis is placed on understanding the effectiveness of these approaches and identifying areas for improvement. Through this comprehensive analysis, the study seeks to contribute to the development of fairer and more transparent AI systems.