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Recent advancements in text-to-image (T2I) models, particularly those utilizing generative adversarial networks (GANs), have significantly improved the synthesis of realistic images across various domains. However, existing GAN-based T2I models still encounter key challenges, such as difficulty in capturing long-range dependencies, vanishing gradients, and the limitations of sequential processing. To address these issues, we introduce BLM-SGAN, a novel model that incorporates Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation. BLM-SGAN leverages BERT's attention mechanisms to capture rich contextual information and efficiently manage extended sequences. 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