Understanding social media discussions about African development is crucial for policy-makers. This study analyses trends in these discussions by (1) reviewing data collection methods, (2) extracting key topics, themes, and sentiments, and (3) applying temporal analysis to observe trends and predict future trends. We compiled a dataset with 22,036 records from Twitter (X) and YouTube. Using BERTopic and Llama for topic extraction, we identified unique topics in six themes: poverty, hunger, education, employment, health, and security. Sentiment analysis was conducted with validation from human-annotated labels. By temporal analysis, we observed and predicted future trends for social concerns using Prophet model. We achieved a coherence score of 0.65 C-v for 304 topics, with 0.82 Kappa agreement. Llama2 outperformed BERTScore in theme extraction, with “Poverty” scoring an F1 of 0.89, followed by “Health” (0.76). Llama had a precision of 0.72 and balanced accuracy of 0.55 in sentiment analysis. Temporal analysis showed steady trends in poverty and security, with growing interest in health, education, and jobs. Positive sentiment peaked in 2020 around youth leadership, while governance and corruption remained stable. Findings inform policy decisions on Africa’s development, and future work will explore key social entities within posts.