The restoration of abnormal speech for individuals with speech impairments using deep learning has emerged as a promising area of research, leveraging advancements in artificial intelligence and neural networks. Traditional methods, such as voice banking and adaptation, while useful, have limitations in preserving speaker identity and addressing severe speech impairments. Recent deep learning approaches, such as multimodal frameworks, GANs, and sequence-to-sequence models, have demonstrated significant improvements in speech intelligibility, clarity, and naturalness. Problems like data scarcity and impairment variability are tackled by these models, which extract and reconstruct linguistic features from dysarthric speech. The review highlights the opportunities presented by these methods, which have the ability to transform Assistive Technologies and provide the speech-impaired patient with more accessible or practical communicational tools.