Machine learning models often perpetuate societal biases present in training data, leading to unfair outcomes in critical applications such as hiring, lending, and healthcare. This paper proposes a novel Parent-Child Framework inspired by the neural plasticity and unbiased learning of children. The framework consists of two components: the NeuroChild, a dynamically adaptive neural network that mimics childlike learning, and the GuardianNet, a feedback-based mechanism that guides the child to reject biased information. By combining dynamic architectures, feedback mechanism, and interpretability through Large Language Models (LLMs), our approach proactively mitigates bias at its root, ensuring fair and transparent machine learning. This work operationalizes the principles of Sustainable, Accurate, Fair, and Explainable Machine Learning (SAFE-ML), advancing them through biologically inspired plasticity and guardian-based guidance, and offers a groundbreaking direction for ethical AI development. While promising, the framework’s computational overhead in real-time adaptation requires further optimization.