Image caption generation involves developing an appropriate textual description of an image through the combination of visual and textual information. Here, a deep learning pipeline with an encoder–decoder architecture is discussed, which uses a deep learning model, such as a convolutional neural network (for instance, ResNet50), to obtain feature representations from an image, and a sequence learning model that employs Long Short-Term Memory (LSTM) to generate the textual description of the image. Spatial attention is incorporated into the decoder to help generate more relevant and detailed captions by associating model attention across important image regions. The pipeline is evaluated using standard evaluation metrics such as BLEU, METEOR, and CIDEr, which provide scores showing how similar the newly generated captions are to human captions/annotations. Demonstrations on the standard Flickr8k dataset show that this approach produces fluent, accurate, and informative descriptions and discuss future applications of the approach, including accessibility, automated tagging, and human–computer interaction.