In recent times, service robots (SR) have become widely accepted in a variety of fields as an alternative to traditional reception methods. Artificial Intelligence (AI) driven systems are seen as efficient labor alternatives, resulting in several SR models already being developed, primarily designed for English-speaking environments. Despite a large Bangla-speaking population, the exploration and implementation of Bangla language support within AI reception systems remain unexplored due to limited resources, posing significant challenges for deployment. This study presents a novel AI-enabled receptionist framework tailored to Bangla-speaking environments, addressing the limitation of Bangla language resources for developing automated reception systems. Leveraging advanced AI technologies including Face Recognition, Speaker Recognition, Automatic Speech Recognition (ASR), Text-to-Speech Synthesis, and Question Answering System, our integrated system demonstrates promise to automate Bangla reception systems. Our suite of models yields positive performance, with a face recognition accuracy of 99.38%, a speaker recognition system with 5.83 Equal Error Rate (ERR), ASR model achieving a Word Error Rate (WER) of 9.005%, TTS model scoring a Mean Opinion Score (MOS) of 4.10, and a question-answering system with a validation loss of 0.03%. Real-world evaluation among 1664 users in a university setting achieved over 75% user satisfaction, with 88% expressing interest in real-life implementation, showcasing usability across different domains. Limitations such as limited training data, scalability, and environmental sensitivity persist, underscoring the need for further development. Nevertheless, our framework demonstrates potential for real-life implementation, fostering human-robot interaction in Bangla-speaking contexts and paving the way for future innovations in AI-driven Bangla receptionist systems.