Dysarthria is a speech disorder influenced by problems in controlling muscles, like the tongue and lips that are desirable to give speech. The motor-speech system can be considered by paralysis, coordination deficiencies, or weakness in dysarthria, which decreases the naturalness, effectiveness, and lucidity of verbal communication. Speech analytic methods depending on deep learning (DL) are common in medical testing. DL has been recently accepted and extensively applied in healthcare treatments. To accurately and objectively identify patients with dysarthria, most of the investigators are utilizing DL to improve the automated detection of dysarthria. Some researchers utilize words for speech detection and various feature extraction techniques to remove features from speech signals. In this paper, we offer an Innovative Framework for Early Detection of Speech Disorders in Children with Speech Impairments Using Deep Learning and Hybrid Heuristic Search Algorithms (EDSDCSI-DLHHS) model. The EDSDCSI-DLHHS model aims to develop an effective system for recognizing speech disorders in children with dysarthria. Initially, the EDSDCSI- DLHHS approach is applied preprocessing techniques are employed to enhance the quality and remove any noise from the recorded speech samples. For feature extraction, linear prediction coding (LPC) has been applied to capture the key features of the speech signals. For the speech disorder classification, the graph attention network (GAT) method is employed. Eventually, the hyperparameter choice of the GAT method is applied by the design of the hybrid Siberian tiger and sand cat swarm optimization (hybrid STSC) algorithm. To demonstrate the goodclassification outcome of the EDSDCSI-DLHHS model, a broad range of experiments occurs on the benchmark dataset. The extensive comparative results ensured the betterment of the EDSDCSI-DLHHS approach across the existing methods.