  Machine learning models may inadvertently memorize sensitive, unauthorized,
or malicious data, posing risks of privacy breaches, security vulnerabilities,
and performance degradation. To address these issues, machine unlearning has
emerged as a critical technique to selectively remove specific training data
points' influence on trained models. This paper provides a comprehensive
taxonomy and analysis of the solutions in machine unlearning. We categorize
existing solutions into exact unlearning approaches that remove data influence
thoroughly and approximate unlearning approaches that efficiently minimize data
influence. By comprehensively reviewing solutions, we identify and discuss
their strengths and limitations. Furthermore, we propose future directions to
advance machine unlearning and establish it as an essential capability for
trustworthy and adaptive machine learning models. This paper provides
researchers with a roadmap of open problems, encouraging impactful
contributions to address real-world needs for selective data removal.
