The detection of anemia from anemic retinopathy involves the identification and analysis of specific retinal changes that occur as a result of severe anemia which requires analysis of multimodal sources. Existing models for identification of these conditions either showcase high complexity, or have lower efficiency when evaluated on clinical scans. Moreover, most of these models cannot be scaled for multi-disease images, which limits their applicability under real-time use cases. To overcome these issues, this research article proposes the design of an efficient and novel multidomain feature analysis model for the estimation of anemic conditions with the help of deep transfer learning process. The proposed model initially represents retinal image scans into multiple domains via estimation of Frequency, Gabor, Convolution, Wavelet and Entropy features. These features are selected via Bacterial Foraging Optimizer (BFO), which assists in retaining high variance feature sets. Due to this, the model is able to reduce redundancies, that assists in improving the speed and accuracy of classification operations. To perform this task, the model uses a set of customized 1-D Cascaded Binary CNN, that assist in categorizing input images into 4 different classes. These include, “Anemic due to Glaucoma”, “Glaucoma”, “Anemic”, and “Normal”, with 98.3% accuracy which is 4.5% higher than existing methods. The model is also able to improve the precision by 2.9%, recall by 3.5%, while reducing the delay needed for classification by 5.9% when compared with existing techniques. Due to which, the model is highly useful for clinical deployments.