This study offers a computational examination of gendered symbolism and recurring motifs in Penina Muhando’s Nguzo Mama, demonstrating how digital methods can deepen traditional literary interpretation. Employing natural language processing (NLP), topic modeling, and sentiment analysis, the research analyzes patterns of gendered language, thematic structures, and character interactions to uncover how femininity, motherhood, authority, and social agency are symbolically constructed within the narrative. The analysis identifies consistent lexical clusters and thematic networks that foreground women’s roles and highlight tensions embedded in socio-cultural expectations. Sentiment mapping and motif frequency analysis reveal how symbolic imagery and narrative positioning contribute to the negotiation of gender identities and power relations. These findings show that computational techniques can detect latent structures and recurring narrative patterns that may not be immediately apparent through close reading alone. Rather than replacing qualitative interpretation, the study positions computational analysis as a complementary and scalable framework that enhances interpretive depth. By integrating feminist literary theory with digital humanities methodologies, the research contributes to interdisciplinary scholarship and proposes a replicable model for analysing gender representation in literary texts. The study also reflects on methodological considerations, including algorithmic bias, contextual sensitivity, and the interpretive limits of quantitative analysis, underscoring the importance of balanced, theoretically informed application of digital tools in literary studies.