This article critically reviews the challenges and limitations of Machine Learning (ML), arguing that while ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science due to its lack of causal explanations. ML models, such as Artificial Neural Networks (ANNs), excel at prediction but often lack transparency, making it difficult to understand the reasoning behind their decisions. The article challenges the notion that ML can simply learn from data through statistical methods, emphasizing that learning involves understanding the underlying skill or ability. It also highlights that ML models may be trained on biased or insufficient data, leading to ineffective or unfair outcomes. However, innovative ML techniques like reinforcement learning and imitation learning show similarities to human learning processes. The article discusses various ML paradigms, including supervised, unsupervised, reinforcement, imitation, and semi-supervised learning, and their applications. It also explores the implications of bias, fairness, and explainable AI in ML, emphasizing the need for ongoing discussions in the ML community. The article concludes that while ML has made significant advancements, it remains a complex field with many challenges that require further research and development.This article critically reviews the challenges and limitations of Machine Learning (ML), arguing that while ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science due to its lack of causal explanations. ML models, such as Artificial Neural Networks (ANNs), excel at prediction but often lack transparency, making it difficult to understand the reasoning behind their decisions. The article challenges the notion that ML can simply learn from data through statistical methods, emphasizing that learning involves understanding the underlying skill or ability. It also highlights that ML models may be trained on biased or insufficient data, leading to ineffective or unfair outcomes. However, innovative ML techniques like reinforcement learning and imitation learning show similarities to human learning processes. The article discusses various ML paradigms, including supervised, unsupervised, reinforcement, imitation, and semi-supervised learning, and their applications. It also explores the implications of bias, fairness, and explainable AI in ML, emphasizing the need for ongoing discussions in the ML community. The article concludes that while ML has made significant advancements, it remains a complex field with many challenges that require further research and development.