Emotion detection is an important text classification task with applications in sentiment analysis, social media monitoring, human-computer interaction, and affective language understanding. However, Punjabi written in the Shahmukhi script remains severely under-resourced for emotion detection, with limited benchmark-style resources available for supervised evaluation. This paper introduces ShahiEmotion, a new Punjabi Shahmukhi emotion detection dataset containing 30379 sentence-level instances annotated with seven emotion categories: sadness, surprise, happiness, anger, neutral, fear, and disgust. The dataset is designed to support research in a low-resource setting characterized by script-specific challenges, lexical variation, and substantial class imbalance. We establish baseline results using several pretrained transformer-based models and formulate emotion detection as a sentence-level classification task. In particular, we fine-tune multilingual BERT, multilingual DistilBERT, XLM-RoBERTa, and Urdu RoBERTa under the same training and evaluation setting using standard cross-entropy loss. Experimental results show that XLM-RoBERTa provides the strongest overall performance among the compared models. The best model achieves 77.95% accuracy, 58.47% macro-F1, and 77.60% weighted-F1 on the test set. The dataset, evaluation protocol, and baseline results introduced in this work are intended to support future research on Punjabi Shahmukhi emotion analysis and low-resource NLP.