Over the past few years there has been a tremendous increase in the worldwide prevalence of goiter and thyroid-based ailments, hitting people belonging to vast ages. Since the thyroid gland is instrumental in the regulation of most important metabolic processes it is pertinent that thyroid malfunctions be identified early and correctly. Nevertheless, although there are numerous available diagnostic algorithms that may be used, they either could only signal a narrow range of thyroid disorders or did not cover other problems like dataset imbalance in classes. This paper develops a diagnostic model that is robust and flexible, by incorporating the deep feature utilization performance of ResNet101 and the ensemble learning powerfulness of the AdaBoost classifier. The suggestion of ResNet101+ AdaBoost can be used successfully to detect ten classes of goiter and thyroid disorders in a single framework. With an enviable 96% accuracy, which when compared with base classifiers like Naive Bayes (88%) and Logistic Regression (91%).The ResNet101+AdaBoost model is highly reliable as a diagnostic tool because it overcomes data imbalance and identifies even intricate patterns in medical imaging. The system promises an application in global healthcare delivery because it detects related conditions such as thyroid and goiter conditions early and improves the outcome of treatment.