The prevalence of ocular disease is high in the current generation. The ocular apparatus holds significant importance within the anatomical structure of the human organism. Failure to address medical conditions could result in visual impairment. Hence, the timely identification and mitigation of ocular disorders can potentially lower the incidence of visual impairment and alleviate the discomfort associated with surgical interventions. This study introduces a thorough examination and an all-encompassing detection framework utilising a multi-level Deep Convolution Neural Network (CNN) module that can be seamlessly incorporated into the current ophthalmologic infrastructure without any hardware modifications. The primary contribution is the development of a Deep CNN based model that, without altering the existing optical illness detection infrastructure, can reliably categorise the types of ocular diseases, notably glaucoma, cataracts, and conjunctivitis. Due to the model’s ease of integration with already available software, such as the Thirona Retina - AI driven Retina picture analyzer and DEDS (Dry Eye Diagnostic System). The experimental findings indicate that the proposed model exhibits a high level of accuracy (98.50%), with great precision (98.91%) in detecting three ocular diseases, namely Cataracts, Glaucoma and conjunctivitis.